Fast Methods for Kernel-based Text Analysis

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iVMS-5200移动监控系统用户手册说明书

iVMS-5200 Mobile SurveillanceVersion 1.1.2SpecificationSpecificationCOPYRIGHT ©2018 Hangzhou Hikvision Digital Technology Co., Ltd.ALL RIGHTS RESERVED.Any and all information, including, among others, wordings, pictures, graphs are the properties of Hangzhou Hikvision Digital Technology Co., Ltd. or its subsidiaries (hereinafter refer red to be “Hikvision”). This user manual (hereinafter referred to be “the Manual”) cannot be reproduced, changed, translated, or distributed, partially or wholly, by any means, without the prior writ ten permission of Hikvision. Unless otherwise stipulated, Hikvision does not make any warranties, guarantees or representations, express or implied, regarding to the Manual.About this ManualThis Manual is applicable to iVMS-5200 Mobile Surveillance.The Manual includes instructions for using and managing the product. Pictures, charts, images and all other information hereinafter are for description and explanation only. The information contained in the Manual is subject to change, without notice, due to firmware updates or other reasons. Please find the latest version in the company website (/en/). Please use this user manual under the guidance of professionals.Trademarks Acknowledgementand other Hikvision’s trademarks and logos are the properties of Hikvision in various jurisdictions. Other trademarks and logos mentioned below are the properties of their respective owners.Legal DisclaimerTO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, THE PRODUCT DESCRIBED, WITH ITS HARDWARE, SOFTWARE AND FIRMWARE, IS PROVIDED “AS IS”, WITH ALL FAULTS AND ERRORS, AND HIKVISION MAKES NO WARRANTIES, EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION, MERCHANTABILITY, SATISFACTORY QUALITY, FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT OF THIRD PARTY. IN NO EVENT WILL HIKVISION, ITS DIRECTORS, OFFICERS, EMPLOYEES, OR AGENTS BE LIABLE TO YOU FOR ANY SPECIAL, CONSEQUENTIAL, INCIDENTAL, OR INDIRECT DAMAGES, INCLUDING, AMONG OTHERS, DAMAGES FOR LOSS OF BUSINESS PROFITS, BUSINESS INTERRUPTION, OR LOSS OF DATA OR DOCUMENTATION, IN CONNECTION WITH THE USE OF THIS PRODUCT, EVEN IF HIKVISION HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.REGARDING TO THE PRODUCT WITH INTERNET ACCESS, THE USE OF PRODUCT SHALL BE WHOLLY AT YOUR OWN RISKS. HIKVISION SHALL NOT TAKE ANY RESPONSIBILITIES FOR ABNORMAL OPERATION, PRIVACY LEAKAGE OR OTHER DAMAGES RESULTING FROM CYBER ATTACK, HACKER ATTACK, VIRUS INSPECTION, OR OTHER INTERNET SECURITY RISKS; HOWEVER, HIKVISION WILL PROVIDE TIMELY TECHNICAL SUPPORT IF REQUIRED.SURVEILLANCE LAWS VARY BY JURISDICTION. PLEASE CHECK ALL RELEVANT LAWS IN YOUR JURISDICTION BEFORE USING THIS PRODUCT IN ORDER TO ENSURE THAT YOUR USE CONFORMS THE APPLICABLE LAW. HIKVISION SHALL NOT BE LIABLE IN THE EVENT THAT THIS PRODUCT IS USED WITH ILLEGITIMATE PURPOSES.IN THE EVENT OF ANY CONFLICTS BETWEEN THIS MANUAL AND THE APPLICABLE LAW, THE LATER PREVAILS.IntroductioniVMS-5200 Mobile Surveillance is applicable to Mobile Surveillance management of the mobile devices including Mobile Video Recorder and portable device. It is capable of adding mobile devices for management, setting alarm linkage, viewing BI report and so on.Key ComponentsService:Central Management ServiceStreaming Service (SMS, optional)Client:iVMS-5200 Mobile Surveillance Web ClientiVMS-5200 Mobile Surveillance Control ClientiVMS-5260M Mobile ClientRunning Environments●For CMS:Operating System: Windows Server 2008 R2 / Windows Server 2012 / Windows 7 / Windows 8 / Windows 8.1 /Windows 10 (64-bit)Processor: E5-2620 series processor with 6 cores (2.0 Ghz)Memory: 8 GBHDD: Enterprise-level SATA disk with 600 GB storage capacityNetwork Controller: RJ45 Gigabit self-adaptive Ethernet interfaces●For Streaming Server:Operating System: Windows Server 2008 R2 / Windows Server 2012 SP2 / Windows 7 / Windows 8 / Windows10 (32/64-bit)Processor: E3-1230 V2 series processor (3.3 GHz)Memory: 8 GBHDD: Enterprise-level SATA disk with at least 10 GB extra capacity for SMS log filesNetwork Controller: RJ45 Gigabit self-adaptive Ethernet interfaces●For Control Client:Operating System: Microsoft Windows 7/Windows 8/Windows 8.1/Windows Server 2008 R2/WindowsServer 2012 (32/64-bit), Windows 10 (64-bit)CPU: Intel Core i3-530 and aboveMemory: 4 GB and aboveVideo Card: Geforce GTX 240 and above●For Browser Version:Internet Explorer 9/10/11 or above (32-bit)Chrome 35/36/37/38/39/40/41/42/43/44 (32-bit)Firefox 32/33/34/35/36/37/38/39/40 (32-bit)●For Mobile Client:iOS: iOS 7.0 and later (since iPhone 4S).Android: Android 4.0 and later.Function FeaturesServerCMS●Provide unified authentication service for clients & servers ●Provide centralized management for mobile devices andservers●Provide the statistics function.●Service manager for system health monitoringSMS (Optional)●Forward and distribute the audio and video data of live view ClientWeb Client●Access to the CMS via iVMS-5200 Mobile Surveillance WebClient●Download Mobile Client by scanning QR code on login page ●Flexible license activation methods: online activation andoffline activation●Startup wizard guides you through basic operationsincluding:Adding mobile devicesSetting recording scheduleConfiguring alarm parametersAdding users●Multiple mobile devices can be added: Mobile VideoRecorder, Portable Video Recorder, Body Camera, and Portable Speed Dome.Add mobile device by single ID or ID Segment●Manage the mobile device by areas●CVR (Central Video Recorder) manageable:Add CVR by IP addressRemotely configure the CVR via web browserOne-touch configuration for setting the CVR storage●RecordingStorage for recording: Mobile Video RecorderTime-based recording and event-based recordingSet recording schedule: All-day Template, Weekday Template, Weekend Template and Custom Template Get the recording schedule configured on device.Copy recording settings (including recording and backup) to other device●BackupBack up the video files stored in the mobile device by uploading them to the added Recording Server Storage: Recording ServerSet backup schedule: All-day Template, Weekday Template, Weekend Template and Custom Template●AlarmConfigure camera alarm, alarm input, server exception, Mobile Video Recorder alarm, and portable device alarm Send emails to notify users of the alarm information with email template configurableSet the arming schedule for the events: All-Day Time-Based Template, All-Day Event-Based Template,and Custom TemplateSet the alarm priority: high, medium, lowSet multiple alarm linkage actions: Trigger pop-up window, audible warning (voice text is supported), alarmoutput linkage, sending email, and sending to MobileClientCopy alarm settings of one device/server to other device/server for quick configurationConfigure map template for fence crossing and deviation alarm●Business IntelligenceMobile Video Recorder Data Analysis: Mileage Statistics / Network Traffic / Online DurationExport/Email/Print the BI statistics●Role & User managementThe default password of the admin user must be changed at first-time loginSupport changing the password of admin userThe admin user can reset other uses’ passwordAdd/Edit/Delete the roles and usersThe roles can be assigned with different permissionsTwo default roles are supported: administrators and operatorsThe name, expiry date and text description can be set for the roles and usersSet live view duration reminding when the live view duration reaches the configured limitCopy the permission settings from default or pre-defined roleTwo types user status are supported: normal and freeze.The users can be assigned with the roles to obtain the corresponding permissionsPTZ control permission level (1~100) can be setDomain users can be imported in batchesThe user can be forced logout by admin●Security settingsLock IP address for certain duration when reaching the configured failed password attemptsSet the min. password strengthSet the max. password age●System Configuration & MaintenanceThe log files can be saved for One Week / Half a Month / One Month / Three Months / Six MonthsSet a static IP address for the WAN accessSet correct LAN IP address for the systemNTP settingsActive directory settingsThe GPS history data can be saved for One Week / Half a Month / One Month / Three Months / Six Months Map API URL can be set for displaying the electronic mapDownload the system logs to view the details for quickly locating the error in case there are problemsDatabase backup and restoreControl Client●Access to the CMS via IP address or domain name●Download Mobile Client by scanning QR code on login page ●Login with domain user●The user account will be frozen after 5 failed passwordattempts●GIS MapLocate the mobile device on mapTrack the mobile device in real timePlay back the history driving patternView the live video of mobile deviceSend message to the mobile device (this function should be supported by the device)Two-way audio with mobile deviceDistance measurementLocate mobile device in the drawn region on mapDisplaying fence crossing region, deviation region, and vehicle driving pattern●Live ViewView real-time video from the mobile devicePop up the reminding when the live view duration reaches the configured limitManual recordingCaptureInstant playbackPTZ control; 256 presets/ 8 patrols/1 patternPTZ control lock/unlockCustom window divisionAuxiliary screen previewDigital zoomTwo-way audioTurn on / off the audio in live view; adjust the volumeSelect main stream or sub stream for live view●PlaybackNormal playback for continuous recordingsSynchronous playback for up to 16 camerasPlayback by timelineDownload the recordings by files/dateMerge the recordings (max. 1G)Playback frame-by-frameSlow forward / fast forwardTurn on / off the audio in playback; adjust the volumeDigital zoomDisplay driving patternVideo clippingCaptureSet the screen layout●Alarm CenterDisplay received alarms in Unacknowledged Alarm panel on home page in real-timeDisplay the alarm name, alarm time, license plate number, and alarm priority in Unacknowledged Alarm Display event alarm info including alarm time, alarm name, alarm status, etc.View the live video from the related cameraPlay back video files from the related cameraView device's moving pattern on the map synchronously when playing back the related videoAdd mark to the alarm informationAcknowledge the event alarm with text descriptionArming control for event alarmAlarm output controlClear the alarm manuallyEnable/Disable the alarm audioEnable/Disable alarm triggered pop-up windowSearch alarm by setting the specified search condition●Health MonitoringStatus overview of the servers, devices, and cameras.Check the online status and HDD status of mobile devicesCheck the online status, signal status and recording status of the camerasCheck online status of Recording ServersCheck CPU usage, RAM usage, and stream status of Streaming Server●Download CenterSearch the recordings by cameras/recording type/time for backupCheck the downloading tasks and status centrallyMerge the recordings footages (max. 1G)Continuous transmission on the breakpointDownload the player for playing back the video files●System maintenance and managementSearch, view and back up the operation logs, system logs, device logs, and message logsConfigure the local parameters--View Scale: Full Screen/4:3/16:9/Original Resolution.--Network Performance: Normal / Better / Best.--Play Performance: Shortest Delay / Self-adaptive--Picture Format: JPEG / BMP--Maximum Mode: Maximize / Full Screen--Enable/Disable Screen Toolbar Display--Enable/Disable Auto-login--Enable/Disable Record Two-way Audio--Enable/Disable Display Real-Time Alarm on GIS Map--Set local saving path of videos / pictures / audios--Set alarm sounds by local audio files or voice engine(require support of the OS)--Lock/Unlock the client--BroadcastMobile Client●Access to the VSM via IP address●Log in with normal user or domain user●The user account will be frozen after 5 failed passwordattempts●View device information ●Locate the vehicles that install the mobile devices on GISmap●Track the vehicles that install the mobile devices in real time ●Search and play the history driving pattern of the vehiclethat installs the mobile device●Add/remove device to/from My Favorites●Live viewView real-time video from the camerasSet 1/4/9/16 window divisionPTZ controlTurn on/off the audio in live viewSet the video qualityManual recordingCaptureDigital zoom●PlaybackSearch by date/storage modePlay back the recordingsTurn on/off the audio in playbackAdjust playback speedVideo clippingCaptureDigital zoom●Receive and display the alarm information and view thealarm related live video or recording or locate the device on map●View/delete/export/share the captured images and videoclips●Provide hardware decoding●Enable the alarm notification to receive the alarminformationPerformance Specification。

生物信息学英文术语及释义总汇

Abstract Syntax Notation (ASN.l)（NCBI发展的许多程序，如显示蛋白质三维结构的Cn3D 等所使用的内部格式）A language that is used to describe structured data types formally, Within bioinformatits,it has been used by the National Center for Biotechnology Information to encode sequences, maps, taxonomic information, molecular structures, and biographical information in such a way that it can be easily accessed and exchanged by computer software.Accession number（记录号）A unique identifier that is assigned to a single database entry for a DNA or protein sequence.Affine gap penalty（一种设置空位罚分策略）A gap penalty score that is a linear function of gap length, consisting of a gap opening penalty and a gap extension penalty multiplied by the length of the gap. Using this penalty scheme greatly enhances the performance of dynamic programming methods for sequence alignment. See also Gap penalty.Algorithm（算法）A systematic procedure for solving a problem in a finite number of steps, typically involving a repetition of operations. Once specified, an algorithm can be written in a computer language and run as a program.Alignment（联配/比对/联配）Refers to the procedure of comparing two or more sequences by looking for a series of individual characters or character patterns that are in the same order in the sequences. Of the two types of alignment, local and global, a local alignment is generally the most useful. See also Local and Global alignments.Alignment score（联配/比对/联配值）An algorithmically computed score based on the number of matches, substitutions, insertions, and deletions (gaps) within an alignment. Scores for matches and substitutions Are derived from a scoring matrix such as the BLOSUM and PAM matrices for proteins, and aftine gap penalties suitable for the matrix are chosen. Alignment scores are in log odds units, often bit units (log to the base 2). Higher scores denote better alignments. See also Similarity score, Distance in sequence analysis.Alphabet（字母表）The total number of symbols in a sequence-4 for DNA sequences and 20 for protein sequences.Annotation（注释）The prediction of genes in a genome, including the location of protein-encoding genes, the sequence of the encoded proteins, anysignificantmatches to other Proteins of known function, and the location of RNA-encoding genes. Predictions are based on gene models; e.g., hidden Markov models of introns and exons in proteins encoding genes, and models of secondary structure in RNA.Anonymous FTP（匿名FTP）When a FTP service allows anyone to log in, it is said to provide anonymous FTP ser-vice. A user can log in to an anonymous FTP server by typing anonymous as the user name and his E-mail address as a password. Most Web browsers now negotiate anonymous FTP logon without asking the user for a user name and password. See also FTP.ASCIIThe American Standard Code for Information Interchange (ASCII) encodes unaccented letters a-z, A-Z, the numbers O-9, most punctuation marks, space, and a set of control characters such as carriage return and tab. ASCII specifies 128 characters that are mapped to the values O-127. ASCII tiles are commonly called plain text, meaning that they only encode text without extra markup.BAC clone（细菌人工染色体克隆）Bacterial artificial chromosome vector carrying a genomic DNA insert, typically 100–200 kb. Most of the large-insert clones sequenced in the project were BAC clones.Back-propagation（反向传输）When training feed-forward neural networks, a back-propagation algorithm can be used to modify the network weights. After each training input pattern is fed through the network, the network’s output is compared with the desired output and the amount of error is calculated. This error is back-propagated through the network by using an error function to correct the network weights. See also Feed-forward neural network.Baum-Welch algorithm（Baum-Welch算法）An expectation maximization algorithm that is used to train hidden Markov models.Baye’s rule（贝叶斯法则）Forms the basis of conditional probability by calculating the likelihood of an event occurring based on the history of the event and relevant background information. In terms of two parameters A and B, the theorem is stated in an equation: The condition-al probability of A, given B, P(AIB), is equal to the probability of A, P(A), times the conditional probability of B, given A, P(BIA), divided by the probability of B, P(B). P(A) is the historical or prior distribution value of A, P(BIA) is a new prediction for B for a particular value of A, and P(B) is the sum of the newly predicted values for B. P(AIB) is a posterior probability, representing a new prediction for A given the prior knowledge of A and the newly discovered relationships between A and B.Bayesian analysis（贝叶斯分析）A statistical procedure used to estimate parameters of an underlyingdistribution based on an observed distribution. S ee also Baye’s rule.Biochips（生物芯片）Miniaturized arrays of large numbers of molecular substrates, often oligonucleotides, in a defined pattern. They are also called DNA microarrays and microchips.Bioinformatics （生物信息学）The merger of biotechnology and information technology with the goal of revealing new insights and principles in biology. /The discipline of obtaining information about genomic or protein sequence data. This may involve similarity searches of databases, comparing your unidentified sequence to the sequences in a database, or making predictions about the sequence based on current knowledge of similar sequences. Databases are frequently made publically available through the Internet, or locally at your institution.Bit score （二进制值/ Bit值）The value S' is derived from the raw alignment score S in which the statistical properties of the scoring system used have been taken into account. Because bit scores have been normalized with respect to the scoring system, they can be used to compare alignment scores from different searches.Bit unitsFrom information theory, a bit denotes the amount of information required to distinguish between two equally likely possibilities. The number of bits of information, AJ, required to convey a message that has A4 possibilities is log2 M = N bits.BLAST （基本局部联配搜索工具，一种主要数据库搜索程序）Basic Local Alignment Search Tool. A set of programs, used to perform fast similarity searches. Nucleotide sequences can be compared with nucleotide sequences in a database using BLASTN, for example. Complex statistics are applied to judge the significance of each match. Reported sequences may be homologous to, or related to the query sequence. The BLASTP program is used to search a protein database for a match against a query protein sequence. There are several other flavours of BLAST. BLAST2 is a newer release of BLAST. Allows for insertions or deletions in the sequences being aligned. Gapped alignments may be more biologically significant.Block（蛋白质家族中保守区域的组块）Conserved ungapped patterns approximately 3-60 amino acids in length in a set of related proteins.BLOSUM matrices（模块替换矩阵，一种主要替换矩阵）An alternative to PAM tables, BLOSUM tables were derived using local multiple alignments of more distantly related sequences than were used for the PAM matrix. These are used to assess thesimilarity of sequences when performing alignments.Boltzmann distribution（Boltzmann 分布）Describes the number of molecules that have energies above a certain level, based on the Boltzmann gas constant and the absolute temperature.Boltzmann probability function(Boltzmann 概率函数)See Boltzmann distribution.Bootstrap analysisA method for testing how well a particular data set fits a model. For example, the validity of the branch arrangement in a predicted phylogenetic tree can be tested by resampling columns in a multiple sequence alignment to create many new alignments. The appearance of a particular branch in trees generated from these resampled sequences can then be measured. Alternatively, a sequence may be left out of an analysis to deter-mine how much the sequence influences the results of an analysis.Branch length（分支长度）In sequence analysis, the number of sequence changes along a particular branch of a phylogenetic tree.CDS or cds （编码序列）Coding sequence.Chebyshe, d inequalityThe probability that a random variable exceeds its mean is less than or equal to the square of 1 over the number of standard deviations from the mean.Clone （克隆）Population of identical cells or molecules (e.g. DNA), derived from a single ancestor.Cloning Vector （克隆载体）A molecule that carries a foreign gene into a host, and allows/facilitates the multiplication of that gene in a host. When sequencing a gene that has been cloned using a cloning vector (rather than by PCR), care should be taken not to include the cloning vector sequence when performing similarity searches. Plasmids, cosmids, phagemids, Y ACs and PACs are example types of cloning vectors.Cluster analysis（聚类分析）A method for grouping together a set of objects that are most similar from a larger group of related objects. The relationships are based on some criterion of similarity or difference. For sequences, a similarity or distance score or a statistical evaluation of those scores is used.CobblerA single sequence that represents the most conserved regions in a multiple sequence alignment. The BLOCKS server uses the cobbler sequence to perform a database similarity search as a way to reach sequences that are more divergent than would be found using the single sequences in the alignment for searches.Coding system (neural networks)Regarding neural networks, a coding system needs to be designed for representing input and output. The level of success found when training the model will be partially dependent on the quality of the coding system chosen.Codon usageAnalysis of the codons used in a particular gene or organism.COG（直系同源簇）Clusters of orthologous groups in a set of groups of related sequences in microorganism and yeast (S. cerevisiae). These groups are found by whole proteome comparisons and include orthologs and paralogs. See also Orthologs and Paralogs.Comparative genomics（比较基因组学）A comparison of gene numbers, gene locations, and biological functions of genes in the genomes of diverse organisms, one objective being to identify groups of genes that play a unique biological role in a particular organism.Complexity (of an algorithm)（算法的复杂性）Describes the number of steps required by the algorithm to solve a problem as a function of the amount of data; for example, the length of sequences to be aligned.Conditional probability（条件概率）The probability of a particular result (or of a particular value of a variable) given one or more events or conditions (or values of other variables).Conservation （保守）Changes at a specific position of an amino acid or (less commonly, DNA) sequence that preserve the physico-chemical properties of the original residue.Consensus（一致序列）A single sequence that represents, at each subsequent position, the variation found within corresponding columns of a multiple sequence alignment.Context-free grammarsA recursive set of production rules for generating patterns of strings. These consist of a set of terminal characters that are used to create strings, a set of nonterminal symbols that correspond to rules and act as placeholders for patterns that can be generated using terminal characters, a set of rules for replacing nonterminal symbols with terminal characters, and a start symbol.Contig （序列重叠群/拼接序列）A set of clones that can be assembled into a linear order. A DNA sequence that overlaps with another contig. The full set of overlapping sequences (contigs) can be put together to obtain the sequence for a long region of DNA that cannot be sequenced in one run in a sequencing assay. Important in genetic mapping at the molecular level.CORBA（国际对象管理协作组制定的使OOP对象与网络接口统一起来的一套跨计算机、操作系统、程序语言和网络的共同标准）The Common Object Request Broker Architecture (CORBA) is an open industry standard for working with distributed objects, developed by the Object Management Group. CORBA allows the interconnection of objects and applications regardless of computer language, machine architecture, or geographic location of the computers.Correlation coefficient（相关系数）A numerical measure, falling between - 1 and 1, of the degree of the linear relationship between two variables. A positive value indicates a direct relationship, a negative value indicates an inverse relationship, and the distance of the value away from zero indicates the strength of the relationship. A value near zero indicates no relationship between the variables.Covariation (in sequences)（共变）Coincident change at two or more sequence positions in related sequences that may influence the secondary structures of RNA or protein molecules.Coverage (or depth) （覆盖率/厚度）The average number of times a nucleotide is represented by a high-quality base in a collection of random raw sequence. Operationally, a 'high-quality base' is defined as one with an accuracy of at least 99% (corresponding to a PHRED score of at least 20).Database（数据库）A computerized storehouse of data that provides a standardized way for locating, adding, removing, and changing data. See also Object-oriented database, Relational database.DendogramA form of a tree that lists the compared objects (e.g., sequences or genes in a microarray analysis) in a vertical order and joins related ones by levels of branches extending to one side of the list.Depth （厚度）See coverageDirichlet mixturesDefined as the conjugational prior of a multinomial distribution. One use is for predicting the expected pattern of amino acid variation found in the match state of a hid-den Markov model (representing one column of a multiple sequence alignment of proteins), based on prior distributions found in conserved protein domains (blocks).Distance in sequence analysis（序列距离）The number of observed changes in an optimal alignment of two sequences, usually not counting gaps.DNA Sequencing （DNA测序）The experimental process of determining the nucleotide sequence of a region of DNA. This is done by labelling each nucleotide (A, C, G or T) with either a radioactive or fluorescent marker which identifies it. There are several methods of applying this technology, each with their advantages and disadvantages. For more information, refer to a current text book. High throughput laboratories frequently use automated sequencers, which are capable of rapidly reading large numbers of templates. Sometimes, the sequences may be generated more quickly than they can be characterised.Domain （功能域）A discrete portion of a protein assumed to fold independently of the rest of the protein andpossessing its own function.Dot matrix（点标矩阵图）Dot matrix diagrams provide a graphical method for comparing two sequences. One sequence is written horizontally across the top of the graph and the other along the left-hand side. Dots are placed within the graph at the intersection of the same letter appearing in both sequences. A series of diagonal lines in the graph indicate regions of alignment. The matrix may be filtered to reveal the most-alike regions by scoring a minimal threshold number of matches within a sequence window.Draft genome sequence （基因组序列草图）The sequence produced by combining the information from the individual sequenced clones (by creating merged sequence contigs and then employing linking information to create scaffolds) and positioning the sequence along the physical map of the chromosomes.DUST （一种低复杂性区段过滤程序）A program for filtering low complexity regions from nucleic acid sequences.Dynamic programming（动态规划法）A dynamic programming algorithm solves a problem by combining solutions to sub-problems that are computed once and saved in a table or matrix. Dynamic programming is typically used when a problem has many possible solutions and an optimal one needs to be found. This algorithm is used for producing sequence alignments, given a scoring system for sequence comparisons.EMBL （欧洲分子生物学实验室，EMBL数据库是主要公共核酸序列数据库之一）European Molecular Biology Laboratories. Maintain the EMBL database, one of the major public sequence databases.EMBnet （欧洲分子生物学网络）European Molecular Biology Network: /was established in 1988, and provides services including local molecular databases and software for molecular biologists in Europe. There are several large outposts of EMBnet, including EXPASY.Entropy（熵）From information theory, a measure of the unpredictable nature of a set of possible elements. The higher the level of variation within the set, the higher the entropy.Erdos and Renyi lawIn a toss of a “fair” coin, the number of heads in a row that can be expected is the logari thm of the number of tosses to the base 2. The law may be generalized for more than two possible outcomes by changing the base of the logarithm to the number of out-comes. This law was used to analyze the number of matches and mismatches that can be expected between random sequences as a basis for scoring the statistical significance of a sequence alignment.EST （表达序列标签的缩写）See Expressed Sequence TagExpect value (E)（E值）E value. The number of different alignents with scores equivalent to or better than S that are expected to occur in a database search by chance. The lower the E value, the more significant the score. In a database similarity search, the probability that an alignment score as good as the one found between a query sequence and a database sequence would be found in as many comparisons between random sequences as was done to find the matching sequence. In other types of sequence analysis, E has a similar meaning.Expectation maximization (sequence analysis)An algorithm for locating similar sequence patterns in a set of sequences. A guessed alignment of the sequences is first used to generate an expected scoring matrix representing the distribution of sequence characters in each column of the alignment, this pattern is matched to each sequence, and the scoring matrix values are then updated to maximize the alignment of the matrix to the sequences. The procedure is repeated until there is no further improvement.Exon （外显子）Coding region of DNA. See CDS.Expressed Sequence Tag (EST) （表达序列标签）Randomly selected, partial cDNA sequence; represents it's corresponding mRNA. dbEST is a large database of ESTs at GenBank, NCBI.FASTA （一种主要数据库搜索程序）The first widely used algorithm for database similarity searching. The program looks for optimal local alignments by scanning the sequence for small matches called "words". Initially, the scores of segments in which there are multiple word hits are calculated ("init1"). Later the scores of several segments may be summed to generate an "initn" score. An optimized alignment that includes gaps is shown in the output as "opt". The sensitivity and speed of the search are inversely related and controlled by the "k-tup" variable which specifies the size of a "word". (Pearson andLipman)Extreme value distribution（极值分布）Some measurements are found to follow a distribution that has a long tail which decays at high values much more slowly than that found in a normal distribution. This slow-falling type is called the extreme value distribution. The alignment scores between unrelated or random sequences are an example. These scores can reach very high values, particularly when a large number of comparisons are made, as in a database similarity search. The probability of a particular score may be accurately predicted by the extreme value distribution, which follows a double negative exponential function after Gumbel.False negative（假阴性）A negative data point collected in a data set that was incorrectly reported due to a failure of the test in avoiding negative results.False positive （假阳性）A positive data point collected in a data set that was incorrectly reported due to a failure of the test. If the test had correctly measured the data point, the data would have been recorded as negative.Feed-forward neural network （反向传输神经网络）Organizes nodes into sequence layers in which the nodes in each layer are fully connected with the nodes in the next layer, except for the final output layer. Input is fed from the input layer through the layers in sequence in a “feed-forward” direction, resulting in output at the final layer. See also Neural network.Filtering (window size)During pair-wise sequence alignment using the dot matrix method, random matches can be filtered out by using a sliding window to compare the two sequences. Rather than comparing a single sequence position at a time, a window of adjacent positions in the two sequences is compared and a dot, indicating a match, is generated only if a certain minimal number of matches occur.Filtering （过滤）Also known as Masking. The process of hiding regions of (nucleic acid or amino acid) sequence having characteristics that frequently lead to spurious high scores. See SEG and DUST.Finished sequence（完成序列）Complete sequence of a clone or genome, with an accuracy of at least 99.99% and no gaps.Fourier analysisStudies the approximations and decomposition of functions using trigonometric polynomials.Format (file)（格式）Different programs require that information be specified to them in a formal manner, using particular keywords and ordering. This specification is a file format.Forward-backward algorithmUsed to train a hidden Markov model by aligning the model with training sequences. The algorithm then refines the model to reduce the error when fitted to the given data using a gradient descent approach.FTP (File Transfer Protocol)（文件传输协议）Allows a person to transfer files from one computer to another across a network using an FTP-capable client program. The FTP client program can only communicate with machines that run an FTP server. The server, in turn, will make a specific portion of its tile system available for FTP access, providing that the client is able to supply a recognized user name and password to the server.Full shotgun clone （鸟枪法克隆）A large-insert clone for which full shotgun sequence has been produced.Functional genomics（功能基因组学）Assessment of the function of genes identified by between-genome comparisons. The function of a newly identified gene is tested by introducing mutations into the gene and then examining the resultant mutant organism for an altered phenotype.gap （空位/间隙/缺口）A space introduced into an alignment to compensate for insertions and deletions in one sequence relative to another. To prevent the accumulation of too many gaps in an alignment, introduction of a gap causes the deduction of a fixed amount (the gap score) from the alignment score. Extension of the gap to encompass additional nucleotides or amino acid is also penalized in the scoring of an alignment.Gap penalty（空位罚分）A numeric score used in sequence alignment programs to penalize the presence of gaps within an alignment. The value of a gap penalty affects how often gaps appear in alignments produced by the algorithm. Most alignment programs suggest gap penalties that are appropriate for particular scoring matrices.Genetic algorithm（遗传算法）A kind of search algorithm that was inspired by the principles of evolution. A population of initial solutions is encoded and the algorithm searches through these by applying a pre-defined fitness measurement to each solution, selecting those with the highest fitness for reproduction. New solutions can be generated during this phase by crossover and mutation operations, defined in the encoded solutions.Genetic map （遗传图谱）A genome map in which polymorphic loci are positioned relative to one another on the basis of the frequency with which they recombine during meiosis. The unit of distance is centimorgans (cM), denoting a 1% chance of recombination.Genome（基因组）The genetic material of an organism, contained in one haploid set of chromosomes.Gibbs sampling methodAn algorithm for finding conserved patterns within a set of related sequences. A guessed alignment of all but one sequence is made and used to generate a scoring matrix that represents the alignment. The matrix is then matched to the left-out sequence, and a probable location of the corresponding pattern is found. This prediction is then input into a new alignment and another scoring matrix is produced and tested on a new left-out sequence. The process is repeated until there is no further improvement in the matrix.Global alignment（整体联配）Attempts to match as many characters as possible, from end to end, in a set of twomore sequences.Gopher (一个文档发布系统，允许检索和显示文本文件)Graph theory（图论）A branch of mathematics which deals with problems that involve a graph or network structure. A graph is defined by a set of nodes (or points) and a set of arcs (lines or edges) joining the nodes. In sequence and genome analysis, graph theory is used for sequence alignments and clustering alike genes.GSS（基因综述序列）Genome survey sequence.GUI（图形用户界面）Graphical user interface.H （相对熵值）H is the relative entropy of the target and background residue frequencies. (Karlin and Altschul, 1990). H can be thought of as a measure of the average information (in bits) available per position that distinguishes an alignment from chance. At high values of H, short alignments can be distinguished by chance, whereas at lower H values, a longer alignment may be necessary. (Altschul, 1991)Half-bitsSome scoring matrices are in half-bit units. These units are logarithms to the base 2 of odds scores times 2.Heuristic（启发式方法）A procedure that progresses along empirical lines by using rules of thumb to reach a solution. The solution is not guaranteed to be optimal.Hexadecimal system（16制系统）The base 16 counting system that uses the digits O-9 followed by the letters A-F.HGMP （人类基因组图谱计划）Human Genome Mapping Project.Hidden Markov Model (HMM)（隐马尔可夫模型）In sequence analysis, a HMM is usually a probabilistic model of a multiple sequence alignment, but can also be a model of periodic patterns in a single sequence, representing, for example, patterns found in the exons of a gene. In a model of multiple sequence alignments, each column of symbols in the alignment is represented by a frequency distribution of the symbols called a state, and insertions and deletions by other states. One then moves through the model along a particular path from state to state trying to match a given sequence. The next matching symbol is chosen from each state, recording its probability (frequency) and also the probability of going to thatparticular state from a previous one (the transition probability). State and transition probabilities are then multiplied to obtain a probability of the given sequence. Generally speaking, a HMM is a statistical model for an ordered sequence of symbols, acting as a stochastic state machine that generates a symbol each time a transition is made from one state to the next. Transitions betweenstates are specified by transition probabilities.Hidden layer（隐藏层）An inner layer within a neural network that receives its input and sends its output to other layers within the network. One function of the hidden layer is to detect covariation within the input data, such as patterns of amino acid covariation that are associated with a particular type of secondary structure in proteins.Hierarchical clustering（分级聚类）The clustering or grouping of objects based on some single criterion of similarity or difference.An example is the clustering of genes in a microarray experiment based on the correlation between their expression patterns. The distance method used in phylogenetic analysis is another example.Hill climbingA nonoptimal search algorithm that selects the singular best possible solution at a given state or step. The solution may result in a locally best solution that is not a globally best solution.Homology（同源性）A similar component in two organisms (e.g., genes with strongly similar sequences) that can be attributed to a common ancestor of the two organisms during evolution.Horizontal transfer（水平转移）The transfer of genetic material between two distinct species that do not ordinarily exchange genetic material. The transferred DNA becomes established in the recipient genome and can be detected by a novel phylogenetic history and codon content com-pared to the rest of the genome.HSP （高比值片段对）High-scoring segment pair. Local alignments with no gaps that achieve one of the top alignment scores in a given search.HTGS/HGT（高通量基因组序列）High-throughout genome sequences。

独立组分分析的十种算法综述及其在药物分析中的应用

独立组分分析的十种算法综述及其在药物分析中的应用宋清;陆峰【摘要】The principles and applications of ICA methods were reviewed. Firstly, a summary of the background and development prospects of the ICA were described, the definition, basic principles, and ten algorithms of ICA were briefly introduced,and then the practical application of the ICA in pharmaceutical analysis was discussed.%对独立组分分析的原理和应用进行了综述.首先,概要叙述独立组分分析的产生背景和发展前景,简要介绍和评述了独立组分分析的定义、基本原理以及其中的十种算法；然后对独立组分分析在药物分析方面的实际应用进行了讨论.【期刊名称】《药学实践杂志》【年(卷),期】2013(031)001【总页数】5页(P1-4,74)【关键词】独立组分分析;化学计量学;药物分析;盲源分离【作者】宋清;陆峰【作者单位】第二军医大学药学院药物分析教研室,上海200433;解放军211医院药剂科,黑龙江哈尔滨150080;第二军医大学药学院药物分析教研室,上海200433【正文语种】中文【中图分类】R917独立组分分析［1］(independent component analysis，ICA)是20世纪90年代提出的一种解决盲源信号分离问题的有效的信号处理方法，其模型最早是作为线性混合的盲信号分离问题(如鸡尾酒会问题)提出的。

它是在既不知道源信号的分布，又不知道源信号的混合模型的情况下，仅利用一组已知的源信号的混合信号来恢复或提取独立的源信号。

基于Albert_与TextCNN_的中文文本分类研究

第 22卷第 4期2023年 4月Vol.22 No.4Apr.2023软件导刊Software Guide基于Albert与TextCNN的中文文本分类研究李飞鸽，王芳，黄树成（江苏科技大学计算机学院，江苏镇江，212100）摘要：互联网数据众多，为高效管理互联网的海量中文文本数据，提出基于Albert与TextCNN的中文文本分类方法（ATT）。

该方法引入Albert模型解决一词多义问题，使用TF-IDF算法抽取当前文本数据中权重最高的5个词构建整个文档关键词表，将关键词表与Albert生成的词向量进行向量拼接，构成一个融合关键词信息的多义词向量。

并且，在传统TextCNN基础上根据中文语言特点调整卷积核窗口大小以提取文本数据的深层局部特征。

实验表明，ATT模型相较于未加入TF-IDF关键词表、未调整卷积核大小的传统模型，F1值分别提升1.88%和2.26%，为中文文本分类提供了一种新方法。

关键词：向量；文本特征提取；多标签；文本分类DOI：10.11907/rjdk.221591开放科学（资源服务）标识码（OSID）：中图分类号：TP391.1 文献标识码：A文章编号：1672-7800（2023）004-0027-05Research on Chinese Text Classification Based on Albert and TextCNNLI Fei-ge， WANG Fang， HUANG Shu-cheng（School of Computer， Jiangsu University of Science and Technology， Zhenjiang 212100， China）Abstract：Due to the large amount of Internet data， in order to efficiently manage the massive Chinese text data on the Internet， a Chinese text classification method based on Albert and TextCNN （referred to as ATT） is proposed. This method introduces Albert model to solve the problem of polysemy. TF-IDF algorithm is used to extract the five words with the highest weight in the current text data to build the whole docu‐ment keyword table. The keyword table and the word vector generated by Albert are vector spliced to form a polysemy word vector that inte‐grates keyword information. Moreover， on the basis of traditional TextCNN， the window size of convolution kernel is adjusted according to the characteristics of Chinese language to extract the deep local features of text data. The experimental results show that the ATT model， compared with the traditional model without TF-IDF keyword list and without adjusting the size of convolution kernel， increases the F1 value by 1.88% and 2.26% respectively， providing a new method for Chinese text classification.Key Words：word vector； text feature extraction； multi-label； text classification0 引言在移动互联时代，文本数据呈现爆炸式增长。

ch13_2005

There are certain classes of problems for which the FEM is difficult, or even impossible to be applied. problems which involve large geometrical changes or deformations of an analysis model such as inverse shape optimization melting and metal casting moving conductors cracks, etc. The FEM usually requires remeshing in order to insure equality between finite element boundaries and the moving discontinuities. Decreasing of the accuracy, Huge computation time during numerical analysis The main objective for the development of meshless methods (also called mesh free method, MFM) is making approximation based on nodes, not elements.
History of meshless methods (1)
Meshless methods originated about twenty years ago . The starting point is the smooth particle hydrodynamics (SPH) method (Lucy 1977) , who used it for modeling astrophysical phenomena without boundaries such as exploding stars and dust clouds. In 1982, Monaghan presented the method which was explained as a kernel estimate to provide a more rational basis.

人教版八年级上册英语说课稿

【 - 小学作文】【篇一】人教版八年级上册英语说课稿人教版英语八年级上第2单元英文说课稿Unit 2 What’s the matter?Dear judges:Good morning.My name is Wang Min, I’m so glad to stand here to share my ideas about the lesson with you. I will analyze the lesson from the six aspects：teaching content, teaching aims,teaching methods,student analysis,homework and teaching procedures .Part One Teaching content1 .Status And FunctionThis unit is the second unit on << Go for it >> Book2,it focus on a very important grammar point—I have a headache. shouldn’t=shouldnot .The textbook start with the interesting topic -- what's the matter., by this way, it is great helpful to attract the attention of the students.Such a topic is related to daily life, so it is helpful to raise learning interests of students and it will be also helpful to improve their oral English.2. Teaching Key PointsThe key points of the text is to learn some words of our body and disease,and how to express physical discomfort and put forward some suggestions.3.Teaching Difficultiesthe difficult points is how to express physical discomfort and put forward some suggestions.Part Two Teaching Aims1、Knowledge Objects:New words; Some advice; Grammar Focus.2、Ability Objects(1)To develop Ss’ abilities of listening and speaking.(2) To train the Ss’ ability of working in groups.3、Moral Object :To make the students care for others in their daily life and promote their friendship.Part Three Teaching methodsAs a teacher, I insist that the students should be the leader in their learning.so in my class,I will use pictures and word cards to raise their learning interests ,the teaching methods Is use are task-teaching methods and game-teaching methods.Part Four Analysis of studentsThe Ss have learned English for a long tome. They can understand some words and some simple sentences. And this lesson is about health and body , Ss have taken a great interest in it.Part Five HomeworkPractice the conversations and review the Grammar Focus.Part Six Teaching ProceduresStep I Greet the class .T: How are you?Ss: I’m fine. Thank you. How are you?T: I have a cold. What should I do?学生提建议：看医生(see a doctor)休息(have a rest) ,吃药(take some medicines)等，教学以上词汇，为以下对话做铺垫。

英语教师说课比赛ppt课件

Task 3: Group work
Divide the Ss into groups of four and ask each group what they will do for their friends.
Task 1 Fill in the blanks according to the text
2 Help the students to describe friends or friendship with the learned words.
PartⅣ Analysis of teaching theories, methods and aids
Teaching theories:
1 The students are the real masters in class.
PartⅡ Analysis of teaching objectives
1 Knowledge objectives
(1) Enable the Ss to master the new words, phrases and useful expressions.
(2) Get the Ss to have a good understanding of friends and friendship.
Part Ⅰ Analysis of teaching background
1 About the teaching material
The selected teaching material is taken from Unit 2, Book B of English for Vocational School. The topic is about friendship, which is intended to develop the theme of this unit.

Kernel methods in machine learning

a rX iv:mat h /7197v3[mat h.ST]1J ul28The Annals of Statistics 2008,Vol.36,No.3,1171–1220DOI:10.1214/009053607000000677c Institute of Mathematical Statistics ,2008KERNEL METHODS IN MACHINE LEARNING 1By Thomas Hofmann,Bernhard Sch ¨o lkopf and Alexander J.Smola Darmstadt University of Technology ,Max Planck Institute for Biological Cybernetics and National ICT Australia We review machine learning methods employing positive deﬁnite kernels.These methods formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS)of functions deﬁned on the data domain,expanded in terms of a kernel.Working in linear spaces of function has the beneﬁt of facilitating the construction and analysis of learning algorithms while at the same time allowing large classes of functions.The latter include nonlinear functions as well as functions deﬁned on nonvectorial data.We cover a wide range of methods,ranging from binary classiﬁers to sophisticated methods for estimation with structured data.1.Introduction.Over the last ten years estimation and learning meth-ods utilizing positive deﬁnite kernels have become rather popular,particu-larly in machine learning.Since these methods have a stronger mathematical slant than earlier machine learning methods (e.g.,neural networks),there is also signiﬁcant interest in the statistics and mathematics community for these methods.The present review aims to summarize the state of the art on a conceptual level.In doing so,we build on various sources,including Burges [25],Cristianini and Shawe-Taylor [37],Herbrich [64]and Vapnik [141]and,in particular,Sch¨o lkopf and Smola [118],but we also add a fair amount of more recent material which helps unifying the exposition.We have not had space to include proofs;they can be found either in the long version of the present paper (see Hofmann et al.[69]),in the references given or in the above books.The main idea of all the described methods can be summarized in one paragraph.Traditionally,theory and algorithms of machine learning and2T.HOFMANN,B.SCH¨OLKOPF AND A.J.SMOLAstatistics has been very well developed for the linear case.Real world data analysis problems,on the other hand,often require nonlinear methods to de-tect the kind of dependencies that allow successful prediction of properties of interest.By using a positive deﬁnite kernel,one can sometimes have the best of both worlds.The kernel corresponds to a dot product in a(usually high-dimensional)feature space.In this space,our estimation methods are linear,but as long as we can formulate everything in terms of kernel evalu-ations,we never explicitly have to compute in the high-dimensional feature space.The paper has three main sections:Section2deals with fundamental properties of kernels,with special emphasis on(conditionally)positive deﬁ-nite kernels and their characterization.We give concrete examples for such kernels and discuss kernels and reproducing kernel Hilbert spaces in the con-text of regularization.Section3presents various approaches for estimating dependencies and analyzing data that make use of kernels.We provide an overview of the problem formulations as well as their solution using convex programming techniques.Finally,Section4examines the use of reproduc-ing kernel Hilbert spaces as a means to deﬁne statistical models,the focus being on structured,multidimensional responses.We also show how such techniques can be combined with Markov networks as a suitable framework to model dependencies between response variables.2.Kernels.2.1.An introductory example.Suppose we are given empirical data (1)(x1,y1),...,(x n,y n)∈X×Y.Here,the domain X is some nonempty set that the inputs(the predictor variables)x i are taken from;the y i∈Y are called targets(the response vari-able).Here and below,i,j∈[n],where we use the notation[n]:={1,...,n}. Note that we have not made any assumptions on the domain X other than it being a set.In order to study the problem of learning,we need additional structure.In learning,we want to be able to generalize to unseen data points.In the case of binary pattern recognition,given some new input x∈X,we want to predict the corresponding y∈{±1}(more complex output domains Y will be treated below).Loosely speaking,we want to choose y such that(x,y)is in some sense similar to the training examples.To this end,we need similarity measures in X and in{±1}.The latter is easier, as two target values can only be identical or diﬀerent.For the former,we require a function(2)k:X×X→R,(x,x′)→k(x,x′)KERNEL METHODS IN MACHINE LEARNING3Fig. 1.A simple geometric classiﬁcation algorithm:given two classes of points(de-picted by“o”and“+”),compute their means c+,c−and assign a test input x to the one whose mean is closer.This can be done by looking at the dot product between x−c [where c=(c++c−)/2]and w:=c+−c−,which changes sign as the enclosed angle passes throughπ/2.Note that the corresponding decision boundary is a hyperplane(the dotted line)orthogonal to w(from Sch¨o lkopf and Smola[118]).satisfying,for all x,x′∈X,k(x,x′)= Φ(x),Φ(x′) ,(3)whereΦmaps into some dot product space H,sometimes called the featurespace.The similarity measure k is usually called a kernel,andΦis called its feature map.The advantage of using such a kernel as a similarity measure is that it allows us to construct algorithms in dot product spaces.For instance, consider the following simple classiﬁcation algorithm,described in Figure1, where Y={±1}.The idea is to compute the means of the two classes inthe feature space,c+=1n− {i:y i=−1}Φ(x i), where n+and n−are the number of examples with positive and negative target values,respectively.We then assign a new pointΦ(x)to the class whose mean is closer to it.This leads to the prediction ruley=sgn( Φ(x),c+ − Φ(x),c− +b)(4)with b=1n+ {i:y i=+1} Φ(x),Φ(x i)k(x,x i)−12(1n2+ {(i,j):y i=y j=+1}k(x i,x j)).Let us consider one well-known special case of this type of classiﬁer.As-sume that the class means have the same distance to the origin(hence, b=0),and that k(·,x)is a density for all x∈X.If the two classes are4T.HOFMANN,B.SCH¨OLKOPF AND A.J.SMOLAequally likely and were generated from two probability distributions that are estimatedp+(x):=1n− {i:y i=−1}k(x,x i),(6)then(5)is the estimated Bayes decision rule,plugging in the estimates p+ and p−for the true densities.The classiﬁer(5)is closely related to the Support Vector Machine(SVM) that we will discuss below.It is linear in the feature space(4),while in the input domain,it is represented by a kernel expansion(5).In both cases,the decision boundary is a hyperplane in the feature space;however,the normal vectors[for(4),w=c+−c−]are usually rather diﬀerent.The normal vector not only characterizes the alignment of the hyperplane, its length can also be used to construct tests for the equality of the two class-generating distributions(Borgwardt et al.[22]).As an aside,note that if we normalize the targets such thatˆy i=y i/|{j:y j= y i}|,in which case theˆy i sum to zero,then w 2= K,ˆyˆy⊤ F,where ·,· F is the Frobenius dot product.If the two classes have equal size,then up to a scaling factor involving K 2and n,this equals the kernel-target alignment deﬁned by Cristianini et al.[38].2.2.Positive deﬁnite kernels.We have required that a kernel satisfy(3), that is,correspond to a dot product in some dot product space.In the present section we show that the class of kernels that can be written in the form(3)coincides with the class of positive deﬁnite kernels.This has far-reaching consequences.There are examples of positive deﬁnite kernels which can be evaluated eﬃciently even though they correspond to dot products in inﬁnite dimensional dot product spaces.In such cases,substituting k(x,x′) for Φ(x),Φ(x′) ,as we have done in(5),is crucial.In the machine learning community,this substitution is called the kernel trick.Definition1(Gram matrix).Given a kernel k and inputs x1,...,x n∈X,the n×n matrixK:=(k(x i,x j))ij(7)is called the Gram matrix(or kernel matrix)of k with respect to x1,...,x n.Definition2(Positive deﬁnite matrix).A real n×n symmetric matrix K ij satisfyingi,j c i c j K ij≥0(8)for all c i∈R is called positive deﬁnite.If equality in(8)only occurs for c1=···=c n=0,then we shall call the matrix strictly positive deﬁnite.KERNEL METHODS IN MACHINE LEARNING 5Definition 3(Positive deﬁnite kernel).Let X be a nonempty set.A function k :X ×X →R which for all n ∈N ,x i ∈X ,i ∈[n ]gives rise to a positive deﬁnite Gram matrix is called a positive deﬁnite kernel .A function k :X ×X →R which for all n ∈N and distinct x i ∈X gives rise to a strictly positive deﬁnite Gram matrix is called a strictly positive deﬁnite kernel .Occasionally,we shall refer to positive deﬁnite kernels simply as kernels .Note that,for simplicity,we have restricted ourselves to the case of real valued kernels.However,with small changes,the below will also hold for the complex valued case.Since i,j c i c j Φ(x i ),Φ(x j ) = i c i Φ(x i ), j c j Φ(x j ) ≥0,kernels of the form (3)are positive deﬁnite for any choice of Φ.In particular,if X is already a dot product space,we may choose Φto be the identity.Kernels can thus be regarded as generalized dot products.While they are not generally bilinear,they share important properties with dot products,such as the Cauchy–Schwarz inequality:If k is a positive deﬁnite kernel,and x 1,x 2∈X ,thenk (x 1,x 2)2≤k (x 1,x 1)·k (x 2,x 2).(9)2.2.1.Construction of the reproducing kernel Hilbert space.We now de-ﬁne a map from X into the space of functions mapping X into R ,denoted as R X ,viaΦ:X →R X where x →k (·,x ).(10)Here,Φ(x )=k (·,x )denotes the function that assigns the value k (x ′,x )to x ′∈X .We next construct a dot product space containing the images of the inputs under Φ.To this end,we ﬁrst turn it into a vector space by forming linear combinationsf (·)=n i =1αi k (·,x i ).(11)Here,n ∈N ,αi ∈R and x i ∈X are arbitrary.Next,we deﬁne a dot product between f and another function g (·)= n ′j =1βj k (·,x ′j )(with n ′∈N ,βj ∈R and x ′j ∈X )asf,g :=n i =1n ′j =1αi βj k (x i ,x ′j ).(12)To see that this is well deﬁned although it contains the expansion coeﬃcients and points,note that f,g = n ′j =1βj f (x ′j ).The latter,however,does not depend on the particular expansion of f .Similarly,for g ,note that f,g = n i =1αi g (x i ).This also shows that ·,· is bilinear.It is symmetric,as f,g =6T.HOFMANN,B.SCH¨OLKOPF AND A.J.SMOLAg,f .Moreover,it is positive deﬁnite,since positive deﬁniteness of k implies that,for any function f,written as(11),we havef,f =ni,j=1αiαj k(x i,x j)≥0.(13)Next,note that given functions f1,...,f p,and coeﬃcientsγ1,...,γp∈R,we havepi,j=1γiγj f i,f j = p i=1γi f i,p j=1γj f j ≥0.(14)Here,the equality follows from the bilinearity of ·,· ,and the right-hand inequality from(13).By(14), ·,· is a positive deﬁnite kernel,deﬁned on our vector space of functions.For the last step in proving that it even is a dot product,we note that,by(12),for all functions(11),k(·,x),f =f(x)and,in particular, k(·,x),k(·,x′) =k(x,x′). (15)By virtue of these properties,k is called a reproducing kernel(Aronszajn [7]).Due to(15)and(9),we have|f(x)|2=| k(·,x),f |2≤k(x,x)· f,f .(16)By this inequality, f,f =0implies f=0,which is the last property that was left to prove in order to establish that ·,· is a dot product. Skipping some details,we add that one can complete the space of func-tions(11)in the norm corresponding to the dot product,and thus gets a Hilbert space H,called a reproducing kernel Hilbert space(RKHS).One can deﬁne a RKHS as a Hilbert space H of functions on a set X with the property that,for all x∈X and f∈H,the point evaluations f→f(x) are continuous linear functionals[in particular,all point values f(x)are well deﬁned,which already distinguishes RKHSs from many L2Hilbert spaces]. From the point evaluation functional,one can then construct the reproduc-ing kernel using the Riesz representation theorem.The Moore–Aronszajn theorem(Aronszajn[7])states that,for every positive deﬁnite kernel on X×X,there exists a unique RKHS and vice versa.There is an analogue of the kernel trick for distances rather than dot products,that is,dissimilarities rather than similarities.This leads to the larger class of conditionally positive deﬁnite kernels.Those kernels are de-ﬁned just like positive deﬁnite ones,with the one diﬀerence being that their Gram matrices need to satisfy(8)only subject toni=1c i=0.(17)KERNEL METHODS IN MACHINE LEARNING7 Interestingly,it turns out that many kernel algorithms,including SVMs and kernel PCA(see Section3),can be applied also with this larger class of kernels,due to their being translation invariant in feature space(Hein et al.[63]and Sch¨o lkopf and Smola[118]).We conclude this section with a note on terminology.In the early years of kernel machine learning research,it was not the notion of positive deﬁnite kernels that was being used.Instead,researchers considered kernels satis-fying the conditions of Mercer’s theorem(Mercer[99],see,e.g.,Cristianini and Shawe-Taylor[37]and Vapnik[141]).However,while all such kernels do satisfy(3),the converse is not true.Since(3)is what we are interested in, positive deﬁnite kernels are thus the right class of kernels to consider.2.2.2.Properties of positive deﬁnite kernels.We begin with some closure properties of the set of positive deﬁnite kernels.Proposition4.Below,k1,k2,...are arbitrary positive deﬁnite kernels on X×X,where X is a nonempty set:(i)The set of positive deﬁnite kernels is a closed convex cone,that is, (a)ifα1,α2≥0,thenα1k1+α2k2is positive deﬁnite;and(b)if k(x,x′):= lim n→∞k n(x,x′)exists for all x,x′,then k is positive deﬁnite.(ii)The pointwise product k1k2is positive deﬁnite.(iii)Assume that for i=1,2,k i is a positive deﬁnite kernel on X i×X i, where X i is a nonempty set.Then the tensor product k1⊗k2and the direct sum k1⊕k2are positive deﬁnite kernels on(X1×X2)×(X1×X2).The proofs can be found in Berg et al.[18].It is reassuring that sums and products of positive deﬁnite kernels are positive deﬁnite.We will now explain that,loosely speaking,there are no other operations that preserve positive deﬁniteness.To this end,let C de-note the set of all functionsψ:R→R that map positive deﬁnite kernels to (conditionally)positive deﬁnite kernels(readers who are not interested in the case of conditionally positive deﬁnite kernels may ignore the term in parentheses).We deﬁneC:={ψ|k is a p.d.kernel⇒ψ(k)is a(conditionally)p.d.kernel},C′={ψ|for any Hilbert space F,ψ( x,x′ F)is(conditionally)positive deﬁnite}, C′′={ψ|for all n∈N:K is a p.d.n×n matrix⇒ψ(K)is(conditionally)p.d.},whereψ(K)is the n×n matrix with elementsψ(K ij).8T.HOFMANN,B.SCH¨OLKOPF AND A.J.SMOLAProposition5.C=C′=C′′.The following proposition follows from a result of FitzGerald et al.[50]for (conditionally)positive deﬁnite matrices;by Proposition5,it also applies for (conditionally)positive deﬁnite kernels,and for functions of dot products. We state the latter case.Proposition6.Letψ:R→R.Thenψ( x,x′ F)is positive deﬁnite for any Hilbert space F if and only ifψis real entire of the formψ(t)=∞ n=0a n t n(18)with a n≥0for n≥0.Moreover,ψ( x,x′ F)is conditionally positive deﬁnite for any Hilbert space F if and only ifψis real entire of the form(18)with a n≥0for n≥1.There are further properties of k that can be read oﬀthe coeﬃcients a n:•Steinwart[128]showed that if all a n are strictly positive,then the ker-nel of Proposition6is universal on every compact subset S of R d in the sense that its RKHS is dense in the space of continuous functions on S in the · ∞norm.For support vector machines using universal kernels,he then shows(universal)consistency(Steinwart[129]).Examples of univer-sal kernels are(19)and(20)below.•In Lemma11we will show that the a0term does not aﬀect an SVM. Hence,we infer that it is actually suﬃcient for consistency to have a n>0 for n≥1.We conclude the section with an example of a kernel which is positive deﬁnite by Proposition6.To this end,let X be a dot product space.The power series expansion ofψ(x)=e x then tells us thatk(x,x′)=e x,x′ /σ2(19)is positive deﬁnite(Haussler[62]).If we further multiply k with the positive deﬁnite kernel f(x)f(x′),where f(x)=e− x 2/2σ2andσ>0,this leads to the positive deﬁniteness of the Gaussian kernelk′(x,x′)=k(x,x′)f(x)f(x′)=e− x−x′ 2/(2σ2).(20)KERNEL METHODS IN MACHINE LEARNING9 2.2.3.Properties of positive deﬁnite functions.We now let X=R d and consider positive deﬁnite kernels of the form(21)k(x,x′)=h(x−x′),in which case h is called a positive deﬁnite function.The following charac-terization is due to Bochner[21].We state it in the form given by Wendland [152].Theorem7.A continuous function h on R d is positive deﬁnite if and only if there exists aﬁnite nonnegative Borel measureµon R d such thath(x)= R d e−i x,ω dµ(ω).(22)While normally formulated for complex valued functions,the theorem also holds true for real functions.Note,however,that if we start with an arbitrary nonnegative Borel measure,its Fourier transform may not be real. Real-valued positive deﬁnite functions are distinguished by the fact that the corresponding measuresµare symmetric.We may normalize h such that h(0)=1[hence,by(9),|h(x)|≤1],in which caseµis a probability measure and h is its characteristic function.For instance,ifµis a normal distribution of the form(2π/σ2)−d/2e−σ2 ω 2/2dω, then the corresponding positive deﬁnite function is the Gaussian e− x 2/(2σ2); see(20).Bochner’s theorem allows us to interpret the similarity measure k(x,x′)= h(x−x′)in the frequency domain.The choice of the measureµdetermines which frequency components occur in the kernel.Since the solutions of kernel algorithms will turn out to beﬁnite kernel expansions,the measureµwill thus determine which frequencies occur in the estimates,that is,it will determine their regularization properties—more on that in Section2.3.2 below.Bochner’s theorem generalizes earlier work of Mathias,and has itself been generalized in various ways,that is,by Schoenberg[115].An important generalization considers Abelian semigroups(Berg et al.[18]).In that case, the theorem provides an integral representation of positive deﬁnite functions in terms of the semigroup’s semicharacters.Further generalizations were given by Krein,for the cases of positive deﬁnite kernels and functions with a limited number of negative squares.See Stewart[130]for further details and references.As above,there are conditions that ensure that the positive deﬁniteness becomes strict.Proposition8(Wendland[152]).A positive deﬁnite function is strictly positive deﬁnite if the carrier of the measure in its representation(22)con-tains an open subset.10T.HOFMANN,B.SCH¨OLKOPF AND A.J.SMOLAThis implies that the Gaussian kernel is strictly positive deﬁnite.An important special case of positive deﬁnite functions,which includes the Gaussian,are radial basis functions.These are functions that can be written as h(x)=g( x 2)for some function g:[0,∞[→R.They have the property of being invariant under the Euclidean group.2.2.4.Examples of kernels.We have already seen several instances of positive deﬁnite kernels,and now intend to complete our selection with a few more examples.In particular,we discuss polynomial kernels,convolution kernels,ANOVA expansions and kernels on documents.Polynomial kernels.From Proposition4it is clear that homogeneous poly-nomial kernels k(x,x′)= x,x′ p are positive deﬁnite for p∈N and x,x′∈R d. By direct calculation,we can derive the corresponding feature map(Poggio [108]):x,x′ p= d j=1[x]j[x′]j p(23)= j∈[d]p[x]j1·····[x]j p·[x′]j1·····[x′]j p= C p(x),C p(x′) ,where C p maps x∈R d to the vector C p(x)whose entries are all possible p th degree ordered products of the entries of x(note that[d]is used as a shorthand for{1,...,d}).The polynomial kernel of degree p thus computes a dot product in the space spanned by all monomials of degree p in the input coordinates.Other useful kernels include the inhomogeneous polynomial, (24)k(x,x′)=( x,x′ +c)p where p∈N and c≥0,which computes all monomials up to degree p.Spline kernels.It is possible to obtain spline functions as a result of kernel expansions(Vapnik et al.[144]simply by noting that convolution of an even number of indicator functions yields a positive kernel function.Denote by I X the indicator(or characteristic)function on the set X,and denote by ⊗the convolution operation,(f⊗g)(x):= R d f(x′)g(x′−x)dx′.Then the B-spline kernels are given by(25)k(x,x′)=B2p+1(x−x′)where p∈N with B i+1:=B i⊗B0.Here B0is the characteristic function on the unit ball in R d.From the deﬁnition of(25),it is obvious that,for odd m,we may write B m as the inner product between functions B m/2.Moreover,note that,for even m,B m is not a kernel.KERNEL METHODS IN MACHINE LEARNING11 Convolutions and structures.Let us now move to kernels deﬁned on struc-tured objects(Haussler[62]and Watkins[151]).Suppose the object x∈X is composed of x p∈X p,where p∈[P](note that the sets X p need not be equal). For instance,consider the string x=AT G and P=2.It is composed of the parts x1=AT and x2=G,or alternatively,of x1=A and x2=T G.Math-ematically speaking,the set of“allowed”decompositions can be thought of as a relation R(x1,...,x P,x),to be read as“x1,...,x P constitute the composite object x.”Haussler[62]investigated how to deﬁne a kernel between composite ob-jects by building on similarity measures that assess their respective parts; in other words,kernels k p deﬁned on X p×X p.Deﬁne the R-convolution of k1,...,k P as[k1⋆···⋆k P](x,x′):=¯x∈R(x),¯x′∈R(x′)P p=1k p(¯x p,¯x′p),(26)where the sum runs over all possible ways R(x)and R(x′)in which we can decompose x into¯x1,...,¯x P and x′analogously[here we used the con-vention that an empty sum equals zero,hence,if either x or x′cannot be decomposed,then(k1⋆···⋆k P)(x,x′)=0].If there is only aﬁnite number of ways,the relation R is calledﬁnite.In this case,it can be shown that the R-convolution is a valid kernel(Haussler[62]).ANOVA kernels.Speciﬁc examples of convolution kernels are Gaussians and ANOVA kernels(Vapnik[141]and Wahba[148]).To construct an ANOVA kernel,we consider X=S N for some set S,and kernels k(i)on S×S,where i=1,...,N.For P=1,...,N,the ANOVA kernel of order P is deﬁned as k P(x,x′):= 1≤i1<···<i P≤N P p=1k(i p)(x i p,x′i p).(27)Note that if P=N,the sum consists only of the term for which(i1,...,i P)= (1,...,N),and k equals the tensor product k(1)⊗···⊗k(N).At the other extreme,if P=1,then the products collapse to one factor each,and k equals the direct sum k(1)⊕···⊕k(N).For intermediate values of P,we get kernels that lie in between tensor products and direct sums.ANOVA kernels typically use some moderate value of P,which speciﬁesthe order of the interactions between attributes x ip that we are interestedin.The sum then runs over the numerous terms that take into account interactions of order P;fortunately,the computational cost can be reduced to O(P d)cost by utilizing recurrent procedures for the kernel evaluation. ANOVA kernels have been shown to work rather well in multi-dimensional SV regression problems(Stitson et al.[131]).12T.HOFMANN,B.SCH¨OLKOPF AND A.J.SMOLABag of words.One way in which SVMs have been used for text categoriza-tion(Joachims[77])is the bag-of-words representation.This maps a given text to a sparse vector,where each component corresponds to a word,and a component is set to one(or some other number)whenever the related word occurs in the ing an eﬃcient sparse representation,the dot product between two such vectors can be computed quickly.Furthermore, this dot product is by construction a valid kernel,referred to as a sparse vector kernel.One of its shortcomings,however,is that it does not take into account the word ordering of a document.Other sparse vector kernels are also conceivable,such as one that maps a text to the set of pairs of words that are in the same sentence(Joachims[77]and Watkins[151]).n-grams and suﬃx trees.A more sophisticated way of dealing with string data was proposed by Haussler[62]and Watkins[151].The basic idea is as described above for general structured objects(26):Compare the strings by means of the substrings they contain.The more substrings two strings have in common,the more similar they are.The substrings need not always be contiguous;that said,the further apart theﬁrst and last element of a substring are,the less weight should be given to the similarity.Depending on the speciﬁc choice of a similarity measure,it is possible to deﬁne more or less eﬃcient kernels which compute the dot product in the feature space spanned by all substrings of documents.Consider aﬁnite alphabetΣ,the set of all strings of length n,Σn,and the set of allﬁnite strings,Σ∗:= ∞n=0Σn.The length of a string s∈Σ∗is denoted by|s|,and its elements by s(1)...s(|s|);the concatenation of s and t∈Σ∗is written st.Denote byk(x,x′)= s#(x,s)#(x′,s)c sa string kernel computed from exact matches.Here#(x,s)is the number of occurrences of s in x and c s≥0.Vishwanathan and Smola[146]provide an algorithm using suﬃx trees, which allows one to compute for arbitrary c s the value of the kernel k(x,x′) in O(|x|+|x′|)time and memory.Moreover,also f(x)= w,Φ(x) can be computed in O(|x|)time if preprocessing linear in the size of the support vectors is carried out.These kernels are then applied to function prediction (according to the gene ontology)of proteins using only their sequence in-formation.Another prominent application of string kernels is in theﬁeld of splice form prediction and geneﬁnding(R¨a tsch et al.[112]).For inexact matches of a limited degree,typically up toǫ=3,and strings of bounded length,a similar data structure can be built by explicitly gener-ating a dictionary of strings and their neighborhood in terms of a Hamming distance(Leslie et al.[92]).These kernels are deﬁned by replacing#(x,s)KERNEL METHODS IN MACHINE LEARNING13 by a mismatch function#(x,s,ǫ)which reports the number of approximate occurrences of s in x.By trading oﬀcomputational complexity with storage (hence,the restriction to small numbers of mismatches),essentially linear-time algorithms can be designed.Whether a general purpose algorithm exists which allows for eﬃcient comparisons of strings with mismatches in linear time is still an open question.Mismatch kernels.In the general case it is only possible toﬁnd algorithms whose complexity is linear in the lengths of the documents being compared, and the length of the substrings,that is,O(|x|·|x′|)or worse.We now describe such a kernel with a speciﬁc choice of weights(Cristianini and Shawe-Taylor[37]and Watkins[151]).Let us now form subsequences u of strings.Given an index sequence i:= (i1,...,i|u|)with1≤i1<···<i|u|≤|s|,we deﬁne u:=s(i):=s(i1)...s(i|u|). We call l(i):=i|u|−i1+1the length of the subsequence in s.Note that if iis not contiguous,then l(i)>|u|.The feature space built from strings of length n is deﬁned to be H n:=R(Σn).This notation means that the space has one dimension(or coordinate) for each element ofΣn,labeled by that element(equivalently,we can thinkof it as the space of all real-valued functions onΣn).We can thus describe the feature map coordinate-wise for each u∈Σn via(28)[Φn(s)]u:= i:s(i)=uλl(i).Here,0<λ≤1is a decay parameter:The larger the length of the subse-quence in s,the smaller the respective contribution to[Φn(s)]u.The sum runs over all subsequences of s which equal u.For instance,consider a dimension of H3spanned(i.e.,labeled)by the string asd.In this case we have[Φ3(Nasd as)]asd= 2λ5.In theﬁrst string,asd is a contiguous substring.In the second string,it appears twice as a noncontiguous substring of length5in lass das,the two occurrences are las as and la d。

Matlab的第三方工具箱大全

Matlab的第三方工具箱大全(按住CTRL点击连接就可以到达每个工具箱的主页面来下载了)Matlab Toolboxes∙ADCPtools - acoustic doppler current profiler data processing∙AFDesign - designing analog and digital filters∙AIRES - automatic integration of reusable embedded software∙Air-Sea - air-sea flux estimates in oceanography∙Animation - developing scientific animations∙ARfit - estimation of parameters and eigenmodes of multivariate autoregressive methods∙ARMASA - power spectrum estimation∙AR-Toolkit - computer vision tracking∙Auditory - auditory models∙b4m - interval arithmetic∙Bayes Net - inference and learning for directed graphical models∙Binaural Modeling - calculating binaural cross-correlograms of sound∙Bode Step - design of control systems with maximized feedback∙Bootstrap - for resampling, hypothesis testing and confidence interval estimation ∙BrainStorm - MEG and EEG data visualization and processing∙BSTEX - equation viewer∙CALFEM - interactive program for teaching the finite element method∙Calibr - for calibrating CCD cameras∙Camera Calibration∙Captain - non-stationary time series analysis and forecasting∙CHMMBOX - for coupled hidden Markov modeling using max imum likelihood EM ∙Classification - supervised and unsupervised classification algorithms∙CLOSID∙Cluster - for analysis of Gaussian mixture models for data set clustering∙Clustering - cluster analysis∙ClusterPack - cluster analysis∙COLEA - speech analysis∙CompEcon - solving problems in economics and finance∙Complex - for estimating temporal and spatial signal complexities∙Computational Statistics∙Coral - seismic waveform analysis∙DACE - kriging approximations to computer models∙DAIHM - data assimilation in hydrological and hydrodynamic models∙Data Visualization∙DBT - radar array processing∙DDE-BIFTOOL - bifurcation analysis of delay differential equations∙Denoise - for removing noise from signals∙DiffMan - solv ing differential equations on manifolds∙Dimensional Analysis -∙DIPimage - scientific image processing∙Direct - Laplace transform inversion via the direct integration method∙DirectSD - analysis and design of computer controlled systems with process-oriented models∙DMsuite - differentiation matrix suite∙DMTTEQ - design and test time domain equalizer design methods∙DrawFilt - drawing digital and analog filters∙DSFWAV - spline interpolation with Dean wave solutions∙DWT - discrete wavelet transforms∙EasyKrig∙Econometrics∙EEGLAB∙EigTool - graphical tool for nonsymmetric eigenproblems∙EMSC - separating light scattering and absorbance by extended multiplicative signal correction∙Engineering Vibration∙FastICA - fixed-point algorithm for ICA and projection pursuit∙FDC - flight dynamics and control∙FDtools - fractional delay filter design∙FlexICA - for independent components analysis∙FMBPC - fuzzy model-based predictive control∙ForWaRD - Fourier-wavelet regularized deconvolution∙FracLab - fractal analysis for signal processing∙FSBOX - stepwise forward and backward selection of features using linear regression∙GABLE - geometric algebra tutorial∙GAOT - genetic algorithm optimization∙Garch - estimating and diagnosing heteroskedasticity in time series models∙GCE Data - managing, analyzing and displaying data and metadata stored using the GCE data structure specification∙GCSV - growing cell structure visualization∙GEMANOVA - fitting multilinear ANOVA models∙Genetic Algorithm∙Geodetic - geodetic calculations∙GHSOM - growing hierarchical self-organizing map∙glmlab - general linear models∙GPIB - wrapper for GPIB library from National Instrument∙GTM - generative topographic mapping, a model for density modeling and data visualization∙GVF - gradient vector flow for finding 3-D object boundaries∙HFRadarmap - converts HF radar data from radial current vectors to total vectors ∙HFRC - importing, processing and manipulating HF radar data∙Hilbert - Hilbert transform by the rational eigenfunction expansion method∙HMM - hidden Markov models∙HMMBOX - for hidden Markov modeling using maximum likelihood EM∙HUTear - auditory modeling∙ICALAB - signal and image processing using ICA and higher order statistics∙Imputation - analysis of incomplete datasets∙IPEM - perception based musical analysisJMatLink - Matlab Java classesKalman - Bayesian Kalman filterKalman Filter - filtering, smoothing and parameter estimation (using EM) for linear dynamical systemsKALMTOOL - state estimation of nonlinear systemsKautz - Kautz filter designKrigingLDestimate - estimation of scaling exponentsLDPC - low density parity check codesLISQ - wavelet lifting scheme on quincunx gridsLKER - Laguerre kernel estimation toolLMAM-OLMAM - Levenberg Marquardt with Adaptive Momentum algorithm for training feedforward neural networksLow-Field NMR - for exponential fitting, phase correction of quadrature data and slicing LPSVM - Newton method for LP support vector machine for machine learning problems LSDPTOOL - robust control system design using the loop shaping design procedure LS-SVMlabLSVM - Lagrangian support vector machine for machine learning problemsLyngby - functional neuroimagingMARBOX - for multivariate autogressive modeling and cross-spectral estimation MatArray - analysis of microarray dataMatrix Computation- constructing test matrices, computing matrix factorizations, visualizing matrices, and direct search optimizationMCAT - Monte Carlo analysisMDP - Markov decision processesMESHPART - graph and mesh partioning methodsMILES - maximum likelihood fitting using ordinary least squares algorithmsMIMO - multidimensional code synthesisMissing - functions for handling missing data valuesM_Map - geographic mapping toolsMODCONS - multi-objective control system designMOEA - multi-objective evolutionary algorithmsMS - estimation of multiscaling exponentsMultiblock - analysis and regression on several data blocks simultaneously Multiscale Shape AnalysisMusic Analysis - feature extraction from raw audio signals for content-based music retrievalMWM - multifractal wavelet modelNetCDFNetlab - neural network algorithmsNiDAQ - data acquisition using the NiDAQ libraryNEDM - nonlinear economic dynamic modelsNMM - numerical methods in Matlab textNNCTRL - design and simulation of control systems based on neural networks NNSYSID - neural net based identification of nonlinear dynamic systemsNSVM - newton support vector machine for solv ing machine learning problems NURBS - non-uniform rational B-splinesN-way - analysis of multiway data with multilinear modelsOpenFEM - finite element developmentPCNN - pulse coupled neural networksPeruna - signal processing and analysisPhiVis- probabilistic hierarchical interactive visualization, i.e. functions for visual analysis of multivariate continuous dataPlanar Manipulator - simulation of n-DOF planar manipulatorsPRT ools - pattern recognitionpsignifit - testing hyptheses about psychometric functionsPSVM - proximal support vector machine for solving machine learning problems Psychophysics - vision researchPyrTools - multi-scale image processingRBF - radial basis function neural networksRBN - simulation of synchronous and asynchronous random boolean networks ReBEL - sigma-point Kalman filtersRegression - basic multivariate data analysis and regressionRegularization ToolsRegularization Tools XPRestore ToolsRobot - robotics functions, e.g. kinematics, dynamics and trajectory generation Robust Calibration - robust calibration in statsRRMT - rainfall-runoff modellingSAM - structure and motionSchwarz-Christoffel - computation of conformal maps to polygonally bounded regions SDH - smoothed data histogramSeaGrid - orthogonal grid makerSEA-MAT - oceanographic analysisSLS - sparse least squaresSolvOpt - solver for local optimization problemsSOM - self-organizing mapSOSTOOLS - solving sums of squares (SOS) optimization problemsSpatial and Geometric AnalysisSpatial RegressionSpatial StatisticsSpectral MethodsSPM - statistical parametric mappingSSVM - smooth support vector machine for solving machine learning problems STATBAG - for linear regression, feature selection, generation of data, and significance testingStatBox - statistical routinesStatistical Pattern Recognition - pattern recognition methodsStixbox - statisticsSVM - implements support vector machinesSVM ClassifierSymbolic Robot DynamicsTEMPLAR - wavelet-based template learning and pattern classificationTextClust - model-based document clusteringTextureSynth - analyzing and synthesizing visual texturesTfMin - continous 3-D minimum time orbit transfer around EarthTime-Frequency - analyzing non-stationary signals using time-frequency distributions Tree-Ring - tasks in tree-ring analysisTSA - uni- and multivariate, stationary and non-stationary time series analysisTSTOOL - nonlinear time series analysisT_Tide - harmonic analysis of tidesUTVtools - computing and modifying rank-revealing URV and UTV decompositions Uvi_Wave - wavelet analysisvarimax - orthogonal rotation of EOFsVBHMM - variation Bayesian hidden Markov modelsVBMFA - variational Bayesian mixtures of factor analyzersVMT- VRML Molecule Toolbox, for animating results from molecular dynamics experimentsVOICEBOXVRMLplot - generates interactive VRML 2.0 graphs and animationsVSVtools - computing and modifying symmetric rank-revealing decompositions WAFO - wave analysis for fatique and oceanographyWarpTB - frequency-warped signal processingWAVEKIT - wavelet analysisWaveLab - wavelet analysisWeeks - Laplace transform inversion via the Weeks methodWetCDF - NetCDF interfaceWHMT - wavelet-domain hidden Markov tree modelsWInHD - Wavelet-based inverse halftoning via deconvolutionWSCT - weighted sequences clustering toolkitXMLTree - XML parserYAADA - analyze single particle mass spectrum dataZMAP - quantitative seismicity analysis。

新视野大学英语读写教程book4 unit3教案

Book4 Unit 3I. Teaching Objectives1. To know the meaning and usage of some important words, phrases and patterns2. To be familiar with the writing skills of the text and make use of it in writing3. To improve Ss reading skills by studying section B4. To respond and cooperate with classmates willingly5. To participate actively6. To read sentences and texts with proper intonation7. To write smoothly and legiblyII.Teaching Focus1. Useful words, phrases and sentence structures;2. Reading skill: Understanding Figurative Language;3. Writing skill: Structured Writing (P 69)III .Main Teaching Methods and TechniquesUse the CAI (PPT software) and group work; use task-based language teaching method, communicative approach and audio-visual method.V. Teaching ProceduresSection A: Longing for a New Welfare System (Four Periods)Step 1: Pre-reading Activities:1.1 GreetingsGreet the whole classReview(1). Ask students some questions to review the last lesson（show them on thescreen）.(2). Check the homework（get to know the social welfare system of the US and China by surfing the Internet or reading relevant books ）;1.2 Warming upTopics:(1). Getting to know some simple information on social welfare system.(2).ask the Ss to talk about difference between the public health, education andhousing in China and in the US.Step 2: While-reading activities:2.1. Background information:A social welfare provision refers to any program which seeks to provide a guaranteed minimum level of income, service or other support for the population of a country as a whole, or for specific groups such as the poor, elderly, and disabled people. Social welfare programs are undertaken by governments and by non-governmental organizations (NGOs). Social welfare payments and services are provided at the expense of taxpayers generally or by obligatory National Insurance contributions, funded by benefactors. Welfare payments can take the form of in-kind transfers (e.g., health care services) or cash (e.g., earned income tax credit). Examples of social welfare services include the following:•Compulsory superannuation savings programs.•Compulsory social insurance programs, often based on income, to pay for the social welfare service being provided. These are often incorporated into the taxation system and may be inseparable from income tax.•Pensions, either for the entire population or for those who had lower incomes. •Financial aid, including social security and tax relief, to those with low incomes or inability to meet basic living costs, especially those who are raising children, elderly, unemployed, injured, sick or disabled.•Free or low cost nursing, medical and hospital care, antenatal and postnatal care for those who are sick, injured or unable to care for themselves. This may be available to everybody, or means tested. Services may be provided in the community or a medical facility.•Free or low-cost public education for all children, and financial aid, sometimes as a scholarship or pension, sometimes in the form of a suspensory loan, to students attending academic institutions or undertaking vocational training.•The state may also fund or operate social work and community-based organizations that provide services that benefit disadvantaged people in the community.•Welfare money paid by a government to persons who are in need of financial assistance.Purposes:1. To develop Ss’ online learning ability2.To improve Ss’ ability to retrieve the relevant informa tion3. To stimulate Ss’ psychomotor thinking4. To arouse Ss’ interest in learning the unitMethod: Talk in groups; Use task-based language teaching method, communicative approach, and audio-lingual method.2.2 Text Structure Analysis2.2.1 Fast reading:Ask the Ss to read the passage as quickly as they can and to answer the questions on the screen. Let them get the main idea of each paragraph and make clear about the text structure.2.2.2 Main idea: The passage is about longing for a new welfare system.2.2.3 Text structure: (the chart below)(Purpose: Improve the students’ reading and writing ability and understand the general idea of each paragraph.Method: Read the text individually and talk in groups; Use task-based language teaching method, reading approach, communicative approach and total physical response method.)Step 3: Intensive reading:3.1. Ss are required to read the passage carefully again and answer some detailed questions on the screen. 1.3. Lead-in and preparation for reading(1). What kind of person is the author?handicapped; confined to wheelchair; carrying a urine bag everyday;independent; self-respect; self-support; self-made(2). How could the writer possibly get his wheelchair repaired?the handicapped client; caseworker; medical worker; main welfare office; wheelchair repair company(3). What can you conclude from the procedure of asking for wheelchair repairs?very difficult for welfare clients to ask for extra financial help(4). How do you describe Suzanne?arrogant; suspicious; indifferent; careless; business-like; a detectivePurpose: Arouse the students’ interest of study. Bring in new subject: Why is author longing for a new welfare system?Method: Use the CAI, PPT software and talk in groups; Use task-based language teaching method, communicative approach, audio-visual method and audio-lingual method.3.2. Teacher picks out some difficult sentences and language points to explain. 1) Longing for a New Welfare System (Title)long for: have an intense desire for; want very muche.g.①The children are longing for the holidays. 孩子们盼望放假。

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FastMethodsforKernel-basedTextAnalysisTakuKudoandYujiMatsumotoGraduateSchoolofInformationScience,NaraInstituteofScienceandTechnology{taku-ku,matsu}@is.aist-nara.ac.jp

AbstractKernel-basedlearning(e.g.,SupportVec-torMachines)hasbeensuccessfullyap-pliedtomanyhardproblemsinNaturalLanguageProcessing(NLP).InNLP,al-thoughfeaturecombinationsarecrucialtoimprovingperformance,theyareheuris-ticallyselected.Kernelmethodschangethissituation.Themeritofthekernelmethodsisthateffectivefeaturecombina-tionisimplicitlyexpandedwithoutlossofgeneralityandincreasingthecompu-tationalcosts.Kernel-basedtextanalysisshowsanexcellentperformanceintermsinaccuracy;however,thesemethodsareusuallytooslowtoapplytolarge-scaletextanalysis.Inthispaper,weextendaBasketMiningalgorithmtoconvertakernel-basedclassiﬁerintoasimpleandfastlinearclassiﬁer.ExperimentalresultsonEnglishBaseNPChunking,JapaneseWordSegmentationandJapaneseDepen-dencyParsingshowthatournewclassi-ﬁersareabout30to300timesfasterthanthestandardkernel-basedclassiﬁers.

1IntroductionKernelmethods(e.g.,SupportVectorMachines(Vapnik,1995))attractagreatdealofattentionre-cently.IntheﬁeldofNaturalLanguageProcess-ing,manysuccesseshavebeenreported.ExamplesincludePart-of-Speechtagging(Nakagawaetal.,2002)TextChunking(KudoandMatsumoto,2001),NamedEntityRecognition(IsozakiandKazawa,2002),andJapaneseDependencyParsing(KudoandMatsumoto,2000;KudoandMatsumoto,2002).ItisknowninNLPthatcombinationoffeaturescontributestoasigniﬁcantimprovementinaccuracy.Forinstance,inthetaskofdependencyparsing,itwouldbehardtoconﬁrmacorrectdependencyre-lationwithonlyasinglesetoffeaturesfromeitheraheadoritsmodiﬁer.Rather,dependencyrelationsshouldbedeterminedbyatleastinformationfrombothoftwophrases.Inpreviousresearch,featurecombinationhasbeenselectedmanually,andtheperformancesigniﬁcantlydependedontheseselec-tions.Thisisnotthecasewithkernel-basedmethod-ology.Forinstance,ifweuseapolynomialker-nel,allfeaturecombinationsareimplicitlyexpandedwithoutlossofgeneralityandincreasingthecompu-tationalcosts.Althoughthemappedfeaturespaceisquitelarge,themaximalmarginstrategy(Vapnik,1995)ofSVMsgivesusagoodgeneralizationper-formancecomparedtothepreviousmanualfeatureselection.Thisisthemainreasonwhykernel-basedlearninghasdeliveredgreatresultstotheﬁeldofNLP.Kernel-basedtextanalysisshowsanexcellentper-formanceintermsinaccuracy;however,itsinef-ﬁciencyinactualanalysislimitspracticalapplica-tion.Forexample,anSVM-basedNE-chunkerrunsatarateofonly85byte/sec,whilepreviousrule-basedsystemcanprocessseveralkilobytespersec-ond(IsozakiandKazawa,2002).Suchslowexe-cutiontimeisinadequateforInformationRetrieval,QuestionAnswering,orTextMining,wherefastanalysisoflargequantitiesoftextisindispensable.Thispaperpresentstwonovelmethodsthatmakethekernel-basedtextanalyzerssubstantiallyfaster.ThesemethodsareapplicablenotonlytotheNLPtasksbutalsotogeneralmachinelearningtaskswheretrainingandtestexamplesarerepresentedinabinaryvector.Morespeciﬁcally,wefocusonaPolynomialKer-nelofdegreed,whichcanattainfeaturecombina-tionsthatarecrucialtoimprovingtheperformanceoftasksinNLP.Second,weintroducetwofastclas-siﬁcationalgorithmsforthiskernel.OneisPKI(PolynomialKernelInverted),whichisanexten-sionofInvertedIndexinInformationRetrieval.TheotherisPKE(PolynomialKernelExpanded),whereallfeaturecombinationsareexplicitlyexpanded.ByapplyingPKE,wecanconvertakernel-basedclas-siﬁerintoasimpleandfastlinerclassiﬁer.InordertobuildPKE,weextendthePreﬁxSpan(Peietal.,2001),anefﬁcientBasketMiningalgorithm,toenu-merateeffectivefeaturecombinationsfromasetofsupportexamples.ExperimentsonEnglishBaseNPChunking,JapaneseWordSegmentationandJapaneseDepen-dencyParsingshowthatPKIandPKEperformre-spectively2to13timesand30to300timesfasterthanstandardkernel-basedsystems,withoutadis-cerniblechangeinaccuracy.2KernelMethodandSupportVectorMachinesSupposewehaveasetoftrainingdataforabinaryclassiﬁcationproblem:(x1,y1),...,(xL,yL)xj∈󰀊N,yj∈{+1,−1},wherexjisafeaturevectorofthej-thtrainingsam-ple,andyjistheclasslabelassociatedwiththistrainingsample.ThedecisionfunctionofSVMsisdeﬁnedbyy(x)=sgn󰀂󰀈j∈SVyjαjφ(xj)·φ(x)+b󰀃,(1)where:(A)φisanon-linermappingfunctionfrom󰀊Nto󰀊H(N󰀇H).(B)αj,b∈󰀊,αj≥0.Themappingfunctionφshouldbedesignedsuchthatalltrainingexamplesarelinearlyseparablein󰀊Hspace.SinceHismuchlargerthanN,itre-quiresheavycomputationtoevaluatethedotprod-uctsφ(xi)·φ(x)inanexplicitform.Thisproblem

canbeovercomebynoticingthatbothconstructionofoptimalparameterαi(wewillomitthedetailsofthisconstructionhere)andthecalculationofthedecisionfunctiononlyrequiretheevaluationofdotproductsφ(xi)·φ(x).Thisiscritical,since,insome