05_NaiveBayes
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Research on Hierarchical Interactive Teaching Model Based on Naive Bayesian ClassificationDongyan FanInformation faculty, Business College of Shanxi University, Taiyuan 030031, ChinaAbstract—The purpose of this research is improving the current inject classroom teaching mode that ignores individual differences and inefficiency of students. By studying classification algorithm in data mining and applying the classification method based on Naive Bayes algorithm, we designed and implemented scientific classification of students, and draw lessons from stratified and interactive teaching mode, so as to builded a new effective teaching mode. The results show that through scientific classification of students, real-time hierarchical interaction teaching effectively stimulate students' interest in learning, improve cooperation ability, and improve classroom teaching efficiency.Keywords—Naive Bayesian; student classification; hierarchical interactive; teaching modelI.I NTRODUCTIONUnder the background of big data era, the current teaching mode is not adapt to the cultivation of innovative talents, there are many problems, such as low efficiency of classroom, teachers' manipulation of teaching process, ignore the individual differences of students in knowledge transfer ability. Therefore, this study aimed at these problems, by studying classification algorithm in data mining and applying the classification method based on Naive Bayes algorithm, we design and implement scientific classification of students, and draw lessons from stratified and interactive teaching mode, so as to build a new effective teaching mode. The mode enable students to learn efficiently, so as to adapt to the trend of rapid development of new technology and cultivate innovative talents.II.R ESEARCH M ETHODThe research and practice of the hierarchical interactive teaching model based on the Naive Bayesian classification is based on the classification of students' differences. So there are two major tasks need to do: the approaches to the students' difference measurement and grouping and the design of hierarchical interactive teaching framework. Its method flow is shown in Figure I.FIGURE I. RESEARCH METHOD FLOWFirst of all, based on the samples, the naive Bayes algorithm according to the student's attribute value is used to test the students' differences. Then, according to the results to make a scientific difference classification to achieve effective grouping for students. At the same time, the design of the hierarchical interactive teaching framework is carried out by the two subjects (the student is the main body, the teacher is the leading part). Finally, the teaching effect is evaluated and analyzed.III.S TUDENT C LASSIFICATION D ESIGN B ASED ON N AIVEB AYESIANA.Naive Bayesian Theoretical PrincipleAt present, there are many kinds of algorithms in data mining, such as based on Bayes algorithm, decision tree algorithm, neural network algorithm, rough set algorithm, genetic algorithm, support vector machine algorithm and so on. In the practical application of many classification algorithms, the most widely used algorithm is Naive Bayesian algorithm model. Naive Bayes is a simple and effective classification model.From Bayes’ theorem recall that:()()()||P A B P BP B AP A= (1)Equation (1): P(A) and P(B) separate representation the probability of occurrenceof events A andevents B.()|P A B indicates the probability of occurrence of event A under the premise that event B occurs. ()|P A B is a priori probability, and its value is often easily obtained.()|P B A indicates the probability of occurrence of event B under the premise that event A occurs. ()|P B A is a posteriori probability, and its value is the result of the solution of the Bayesian formula.The classifier structure diagram based on the naive Bayes algorithm is shown in Figure II. It’s leaf node Am represents the m attribute, and the root node C represents the category. Suppose {},,D C A S=are training samples, it includes the studentcategory {}12,,iC C C C= and the student attribute {}12,,mA A A A= .Suppose {}12,,nS S S S= represents acollection of classified students, in whichnS represents nthstudent. Suppose {}12,,k mX a a a= is a student to be classified,International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018)in which each m a represents an attribute eigenvalue of the pending item k X .FIGURE II. THE CLASSIFIER STRUCTURE DIAGRAMB. Design the Individualized Attributes of StudentsThe student classification method based on the naive Bayes algorithm is used the information of the past students as the sample set , which is used to construct the naive Bayes classifier.Students are classified according to the information of the students' attributes. The students divided into the same category are not simply using the score as criterion of evaluation. Its are classified by comprehensive evaluation after combination of other attributes.The difference classification based on the naive Bayes algorithm is select the individual attributes of the students as shown in Figure III. The students which 8 attribute values similar in the two dimensions (character and learning style) are put into one category, while the 12 attributes values of the three dimensions of personal basic situation, learning interest and cognitive ability are different. The purpose of the classification is to carry out differential teaching to implicit dynamic stratification and heterogeneous cooperation for students'cognitive ability, learning interest and basic information.FIGURE III. INDIVIDUALIZED ATTRIBUTES OF STUDENTSC. Student Classification Design Based on Naive Bayesian The process based on the naive Bayes classification is shown in Figure IV.FIGURE IV. STUDENT CLASSIFICATION CYCLE FLOW CHARTBASED ON NAIVE BAYES ALGORITHM1)()i P C is set to indicate the frequency of the occurrence of the student category i C in the training sample concentration, that is the category probability. For sample data sets, there are different levels of students in each category, which avoids the discrimination of students.()()i i P C Count C n= (2)The function ()i Count C represents the number of students belonging to category i which is in the entire student sample collection of S .n represents the total number of the entire student sample collection of S .2)()|j j i P A C a = is set to represent the conditional probability of each characteristic attribute value of the student in the category.()()()|i C j j j j i i P A C Count A a a Count C ===(3)j j A a =indicates that the value of the j attribute is j a .Thefunction ()i C j j Count A a =represents the number of students which the attribute name is j A and attribute value is j a in the i student category.3) ()|k i P X C is set to represent the conditional probability of the students k X to be classified in the student category i C , m represents the number of attributes that describe student differences.()()1||mk i j j i j P X C P A C a ===∏ (4)4) ()j j P A a = is set to represent the probability of the student's attribute j A when the value is j a . ()()j j j j P A Count A a a n=== (5)The function ()j j Count A a = indicates the number when the value of attribute j is j a .5) ()k P X is set to indicate the probability that the student k X should be classified in the training sample concentration. ()()1mk j j j P X P A a ===∏ (6)6) ()|i k P C X is set to represent the conditional probability that the student k X should be classified to category i . ()()()()||k i i i k k P X C P C P C X P X =(7)7) ()max |k P C X is set to represent the maximum category probability of the student k X which should be classified to the student category .()()()(){}max 12|max |,|,,|k k k i k P C X P C X P C X P C X = (8) max C indicates the maximum category of conditionalprobability which is obtained by (8).Finally, (8) is used to calculate the maximum category probability of the students to be classified in the students category. That is the category of the students to be classified. At this point, one classification ends.IV. T HE D ESIGN OF THE H IERARCHICAL I NTERACTIVET EACHING F RAMEWORK The hierarchical interactive teaching model is an independent, inquiring and cooperative teaching model based on the classification of the naive Bayes algorithm. This model breaks the original classroom structure, and takes the interaction of teachers and students as the carrier, and also group autonomy, and let the students as the subject of the class. This model is guided by the task of the problem, and it is based on the students' self-study, and it aims at the completion of the task of the group. This model creates an ecological chain class based on group mutual learning to solve problems. It pays attention to the state of learning and the quality of life for every student. The design of the hierarchical interactive teaching model framework is shown in Figure V.FIGURE V. THE HIERARCHICAL INTERACTIVE TEACHING MODELFRAMEWORKThe four layers of the hierarchical interactive teaching model are closely related to each other, and support each other dynamically with the spiral. The five segments drive each other to form a whole, interlace and connect with each other. This teaching mode makes the classroom an active area for teachers and students to resonate with their ideology and to show their personality together.V.A NALYSIS OF T EACHING E FFECTIn this paper, the teaching effect is analyzed from two aspects by using the method of questionnaire and comparative experiment. First, the experimental class's comparative analysis before and after the experiment is carried out. Then, a comparative analysis between the experimental class and the contrast class is carried out.The comparative data of the experimental class before and after the experiment are shown in Figure VI. From Figure VI, it can be seen that 85.72% of the students have An attitude of approval towards the application of the hierarchical interactive teaching model based on the naive Bayes algorithm in the teaching. There are 70.13% of the students satisfied with the improved teaching effect. At the same time, it can be seen that the students' interest in learning and the ability to communicate and cooperate have improved obviously.FIGURE VI. THE COMPARATIVE DATA OF THE EXPERIMENTALCLASS BEFORE AND AFTER THE EXPERIMENT The comparison between the experimental class and the contrast class is shown in Figure VII. From Figure VII, we can see that students' satisfaction degree, teaching effect satisfaction and group learning atmosphere based on Naive Bayes algorithm classification are higher than those of the contrast class. At the same time, it can be seen that the students' interest in learning and the ability to communicate and cooperate have also been improved.FIGURE VII. THE COMPARISON BETWEEN THE EXPERIMENTALCLASS AND THE CONTRAST CLASSVI.C ONCLUSIONThe comprehensive analysis shows that, in the implementation of the hierarchical interactive teaching model based on the naive Bayes algorithm, the new teaching mode was accepted by the students , it was welcomed by the students. The new teaching mode can improve the ability of learning interest and collaboration of students. It has a very good teaching effect. Experiments show that the classification algorithm based on Naive Bayes has better feasibility and effectiveness in solving student classification problem.However, due to the limited personal time and ability, there are still some shortcomings in the study. In order to better achieve hierarchical interaction teaching mode based on Naive Bayes algorithm and improve teaching effect, we still need to further improve the limitation of applying naive Bayes algorithm, that is, suppose the attributes of students are independent.A CKNOWLEDGMENTThis work was supported by “Research and construction of the practice teaching system of information specialty(J2016138, The major project of teaching reform research in Shanxi Education Department)” and “The optimization and the platform construction of the practice teaching system of information specialty (SYJ201509, The major project of the research on teaching reform Business College of Shanxi University)”. Our special thanks are due to Prof. Ma Shangcai, for his helpful discussion with preparing the manuscript.R EFERENCES[1]Jonathan Rauh. Problems in Identifying Public and Private Organizations:A Demonstration Using a Simple Naive Bayesian Classification[J]. PublicOrganization Review,2015,15(1).[2]SangitaB, P., Deshmukh, S.R.. Use of Support Vector Machine, decisiontree and Naive Bayesian techniques for wind speed classification[P].Power and Energy Systems (ICPS), 2011 International Conference on,2011.[3]Yan Dong. Hierarchical interactive teaching mode and its practice andexploration of mathematics teaching in Senior High School [D].Southwest University,2016.[4]Chen Zhiqiang. Hierarchical interactive teaching mode and its practiceand exploration of mathematics teaching in Senior High School [D].Henan University,2016.[5]S. Mukherjee and N. Sharma. Intrusion detection using naïve Bayesclassifier with feature reduction[J].Procedia Technology,vol. 4, pp. 119–128, 2012.[6]L. Jiang, Z. Cai, D. Wang, and H. Zhang. Improving tree augmented naiveBayes for class probability estimation[J]. Knowledge-Based Systems, vol.26, pp. 239–245, 2012.[7]Sharma RK, Sugumaran V, Kumar H, Amarnath M. A comparative studyof naïve Bayes classifier and Bayes net classifier for fault diagnosis of roller bearing using sound signal[J].International Journal of Decision Support Systems. 2015 Jan 1; 1(1):115-29.[8]Hamse Y Mussa, John BO Mitchell,Robert C Glen.Full “Laplacianised”posterior naïve Bayesian algorithm[J]. Journal of Cheminformatics. 2013 5:37.[9]K. Magesh Kumar, P. Valarmathie. Domain and Intelligence BasedMultimedia Question Answering System[J]. International Journal of Evaluation and Research in Education, Vol. 5, No. 3, September 2016 : 227 – 234.[10][11]Zhijun Wang1, Li Chen, Terry Anderson. A Framework forInteraction and Cognitive Engagement in Connectivist Learning Contexts[J]. International Review of Research in Open and DistanceLearning, Vol. 15,No.2, Apr 2014:121-141.。
基于颜色直方图及贝叶斯分类器的制茶工艺的鉴别
基于颜色直方图及贝叶斯分类器的制茶工艺的鉴别摘要：本文介绍了基于颜色直方图及贝叶斯分类器的制茶工艺的鉴别，主要是采集够多的样本图片，提取各类的颜色直方图，并按HSV量化，主要借助于H分量的直方图分布采集相关数据，用贝叶斯公式进行先验概率和后验的计算，应用最小错误概率贝叶斯分类器，判别某一个未知样本属于哪一类的茶。
关键字：颜色直方图贝叶斯公式最小错误概率 HSV分量1 引言人们大都根据茶的香味、茶水润口程度来判断茶叶质量的优劣，一般优质上等茶叶是色香味俱全，且茶水润滑，饮后有口回甘。
颜色是最直观的一个特征，基于颜色的鉴别比较直观也是一般人员力所能及的。
基于颜色直方图鉴别是指利用计算机对采取的茶叶样本提取样本图的颜色直方图，并分别按照HSV分量进行量化，提取量化后颜色直方图H分量的分布状况，结合最小错误概率贝叶斯分类器，以后验概率和前验概率的相似程度，判定未知样本所处的质量等级。
借鉴颜色直方图及贝叶斯分类器，本人利用Matlab设计了一些基于颜色直方图及贝叶斯最小错误概率分类器的算法，选取已知样本图，提取各个样本图的颜色直方图，基于各颜色直方图H分量的量化情况得到的特征参数并未贝叶斯分类器提供必要的数据。
以实地采集的成品茶叶样本来进行颜色直方图绘制及特征显示还有概率分布统计进行制茶工艺的鉴定，事实证明算法的实现有一定的实用性。
二基于颜色直方图及最小错误概率分类的茶叶质量鉴别原理通过图1中的颜色直方图和H分量图，我们可以看到不同的样本图处于不同质量等级的茶叶直方图各异，并且H分量直方图也相同，若能大量提取各个质量等级大部分的样本直方图及H分量，提取到的大量数据，进行概率的统计，均值、方差的求解，接下来利用最小错误概率的贝叶斯分类器大致上能够统计出各个质量等级的概率分布，能够初步验证未知样本大致所处的质量等级。
实现原理及过程如图1所示。
图1 基于颜色直方图及最小错误概率的茶叶质量鉴别原理及过程整个过程主要是要采取大量的样本，进行颜色直方图H分量的提取量化，及其概率分布图的统计，然后通过参数估计过程以最小错误概率的贝叶斯分类器求出未知样图的概率与已知类别中概吕最接近的等级概率。
Active Learning Literature Survey
Active Learning Literature SurveyAnita KrishnakumarUniversity of California,Santa CruzDepartment of Computer Scienceanita@June05,2007AbstractThe most time consuming and expensive task in machine learning is the gatheringof labeled data to train the model or to estimate its parameters.In the real-worldscenario,the availability of labeled data is scarce and we have limited resources tolabel the abundantly available unlabeled data.Hence it makes sense to pick onlythe most informative instances from the unlabeled data and request an expert to pro-vide the label for that instance.Active learning algorithms aim at minimizing theamount of labeled data required to achieve the goal of the machine learning task inhand by strategically selecting the data instance to be labeled by the expert.A lot ofresearch has been conducted in this area over the past two decades leading to greatimprovements in performance of several existing machine learning algorithms andhas also been applied to diversefields like text classification,information retrieval,computer vision and bioinformatics,to name a few.This survey aims at providingan insight into the research in this area and categorizes the diverse algorithms pro-posed based on main characteristics.We also provides a desk where different activelearning algorithms can be compared by evaluation on benchmark datasets.1IntroductionThe central goal of machine learning is to develop systems that can learn from experience or data and improve their performance at some task.In many natural learning tasks,this experience or data is gained interactively,by taking actions,making queries,or doing experiments.Most machine learning research,however,treats the learner as a passiverecipient of the data to be processed.This passive approach ignores the learner’s ability to interact with the environment and gather data.Active learning is the study of how to use this ability effectively.Active learning algorithms have been developed for classification, regression and function optimization and is found to improve the predictive accuracy of several algorithms compared to passive learning.2Major ApproachesThe three major approaches to active learning algorithms are as follows.•Pool-based active learning:As introduced in Lewis and Gale(1994),the learner is provided with a pool of independent and identically distributed unlabeled instances.The active learner at each step chooses an unlabeled instance to request the label from the expert by means of a querying function.This is also called as selective sampling.•Stream-based active learning:The active learner,for example in Freund1997,is presented with a stream of unlabeled instances,from which the learner picks an instance for labeling by the expert.This can be visualized as online pool-based active learning.•Active learning with membership queries:Here,as described in Angluin1988,the active learner asks the expert to classify cases generated by the learning systems.The learner imposes values on the attributes for an instance and observes the re-sponse.This gives the learner theflexibility of framing the data instance that will be the most informative to the learner at that moment.3Characteristics of Active Learning AlgorithmsWe have taken into account several key features that have been addressed in many of the proposed active learning algorithms to compare the effect of each characteristic on the overall performance of the algorithm.3.1Ranker considerationActive learning algorithms may or may not depend on a ranker function to pick the train-ing instance for expert labeling.Several algorithms proposed use support vector machines (SVM),logistic regression,naive Bayes,neural networks,etc.In this section we look atactive learning algorithms from the perspective of the ranker function used in the data instance selection process.Lewis and Gale(1994)describe an uncertainty sampling method where the active learner selects instances whose class membership is most unclear to the learner.Different defini-tions of uncertainty have been used,for example the Query-by-Committee algorithm by Seung et al.(1992),picks those examples for which the selected classifiers disagree,to be labeled by the expert.The authors suggest that their algorithm can be used with any classifier that predicts a class as well as provides a probability estimate of the prediction certainty.Cohn et al.(1995)describe how optimal data selection techniques can be applied to statistically-based learning algorithms like a mixture of Gaussians and locally weighted regression.The algorithm selects instances that if labeled and added to the training set, minimizes the expected error on future test data.The authors show that the statistical models perform more efficiently and accurately than the feedforward neural networks. Similar querying functions have been proposed by Tong and Koller(2000),Campbell et al.(2000)and Schohn and Cohn(2000),called Simple which uses SVMs as the induction component.Here,the querying function is based on the classifier.The algorithm tries to pick instances which are the most informative to the SVM-the support vectors of the dividing hyperplane.This can be thought of as uncertainty sampling where the algorithm selects those instances about which it is most uncertain.In the case of SVMs,the classifier is most uncertain about the examples that are lying close to the margin of the dividing hyperplane.Variations of the Simple algorithm-MaxMin and Ratio methods have been proposed by Tong and Koller(2000),which also use SVM as the learner.Iyengar et al.(2000)present an active learning algorithm that uses adaptive resampling (ALAR)to select instances for expert labeling.In the work described,a probabilistic classifier is usedfirst to determine the degree of uncertainty and then decision trees is used for classification.The experiments considered use the ensemble of classification models generated in each phase or a nearest neighbor(3-nn)as the probabilistic classifier. Roy and McCallum(2001)describe a method to directly maximize the expected error rate reduction,by estimating the future error rate by a loss function.The loss functions help the learner to select those instances that maximize the confidence of the learner about the unlabeled data.Rather than estimating the expected error on the full distribution,this algorithm estimates it over a sample in the pool.The authors base their class probability estimates and classification on naive Bayes,however SVMs or other models with complex parameter space are also recommended.Baram et al.(2003)provide an implementation of this method on SVMs,andfind it to be better than the original naive Bayes algorithm. Logistic regression is used to estimate the class probabilities in the SVM based algorithm. Baram et al.(2003)propose a simple heuristic based on“farthest-first”travel sequences for active learning called Kernel Farthest-First(KFF).Here the active learner selects thatinstance which is farthest away from the current labeled set and can be applied with any classifier learning algorithm.The authors present an application of KFF with an SVM. Mitra et al.(2004)present a probabilistic active learning strategy for support vector ma-chine learning.Identifying and labeling all the true support vectors guarantees low future error.The algorithm uses the k nearest neighbor algorithm to assign a confidence factor c to all instances lying within the boundary,close to the actual support vectors and1−c to interior points which are far from the support vectors.The instances are then chosen probabilistically based on the confidence factor.Nguyen and Smeulders(2004)offer a framework to incorporate clustering into active learning.A classifier is developed based on the cluster representatives and a local noise model propagates the classification decision to the other instances.The model assumes that given the cluster label,the class label of data instance can be inferred.The logistic regression discriminative model is used to estimate the class probability and an isotropic Gaussian model is used to estimate the noise distribution,to propagate information of label from the representatives to the remaining data.A coarse-to-fine strategy is used to adjust the balance between the advantage of large clusters and the accuracy of the data representation.Osugi et al.(2005)propose an active learning algorithm that balances the exploration and exploitation while selecting a new instance for labeling by the expert at each step. The algorithm randomly chooses between exploration and exploitation at each round and receives feedback on the effectiveness of the exploration step,based on the performance of the classifier trained on the explored instance.The active learner updates the probability of exploring in the next phases based on the feedback.The algorithm chooses between the active learners KFF(which explores)and Simple(which exploits),using SVM light as the classifier,with a probability p.If the exploration is a success,resulting in a change in the current hypothesis,then p is maintained with a high value,encouraging more exploration, else p is updated to reduce the probability of exploration.3.2Computational complexityComputational cost is an important factor to be considered in an algorithm.If an algorithm is computationally very expensive,the algorithm might be infeasible for certain real-world applications which are time sensitive.In this section,we consider the time complexities of the above discussed active learning algorithms infinding an optimal instance for labeling. The active learning algorithms with mixture of Gaussians and locally weighted regres-sion proposed by Cohn et al.(1995)performs more effectively than the feedforward neural networks where computing variance estimate and re-training is computationally very expensive.With the mixture of Gaussians,training depends linearly on the number of data instances,but prediction time is independent.On the other hand,for a memory-based model like locally weighted regression,there is no training time,but prediction costs exist.However,both can be enhanced by optimized parallel implementations.The authors Tong and Koller(2000)suggest that the Simple margin active learning al-gorithm is computationally quite efficient.However,improvement gains can be obtained by querying multiple instances at a time as suggested in Lewis and Gale(1994).But the MaxMin and Ratio methods are computationally very expensive.Active learning with adaptive resampling(Iyengar et al.,2000)is computationally very expensive because of the decision trees used in the classification phase.The number of phases,number of points chosen to be labeled and number of adaptive resampling rounds also add to the computational cost,hence the authors have chosen these parameters based on computational complexity and accuracy considerations.The computational complexity to implement the algorithm proposed in Roy and McCal-lum(2001)is described as“hopelessly inefficient”.However,various heuristics approxi-mations and optimizations have been suggested.Some of these approximations are gen-eral and some for the specific implementation by the authors on naive Bayes.The computational complexity of Kernel Farthest-First algorithm of Baram et al.(2003) is quite similar to the Simple margin algorithm.Simple computes the dot product for every unlabeled instance which takes the same time as computing the argmax for KFF.The probabilistic active support vector learning algorithm proposed by Mitra et al.(2004) is computationally more efficient than even the Simple margin algorithm by Tong and Koller(2000),as presented in the comparison by the authors.The active learning using pre-clustering algorithm proposed by Nguyen and Smeulders (2004),use the K-medoid algorithm for clustering as it captures data representation bet-ter.But the K-medoid algorithm is computationally very expensive when the number of clusters or data points is large.However,some simplifications have been presented to reduce the computational cost.The algorithm proposed by Osugi et al.(2005),uses the active learners,Simple or KFF. Hence the time complexity depends on those algorithms.Simple and KFF have similar time complexities and hence,this algorithm has the sample complexity of those algo-rithms per round.3.3DensityIn real-world applications,the data under consideration might have skewed class distri-butions.Some classes might have larger number of samples,and hence a higher density than the other classes.Some classes might have very few instances in the dataset andhence a low density.In this section we analyze whether the active learning algorithms in discussion consider the density of the classes in the dataset,while selecting instances for labeling.The statistical models of Cohn et al.(1995)selects that instance that minimizes the ex-pectation of the learner’s variance on future test set.The instance selection method is independent of density considerations.The Simple algorithm of Tong and Koller(2000),Campbell et al.(2000)and Schohn and Cohn(2000),picks those instances which are close to the dividing hyperplane.Density of the class distribution is ignored here.The ALAR algorithm of Iyengar et al.(2000)also selects instances for expert labeling ignoring the density of samples,as it only considers the degree of uncertainty of the classifier.The algorithm of Roy and McCallum(2001)queries for instances that provide maximal reassurance of the current model.Hence,it does not depend on the class density distribu-tion.The KFF algorithm of Baram et al.(2003)selects those instances that are the farthest from the current set of labeled instances,which does not really take into account the density of samples.The probabilistic active support vector learning algorithm by Mitra et al.(2004)does not take into account the density while querying for instances.The data selection criterion of the active learning algorithm with pre-clustering by Nguyen and Smeulders(2004),gives priority to samples which are cluster representatives,and chooses the ones belonging to high density clustersfibeling of high density clusters contribute to a substantial move of the classification boundary,and hence the algorithm clusters the data into large clusters initially.And once the classification boundary between the large clusters have been obtained,the parameters are adjusted to obtainfiner clustering for a more accurate classification boundary.The active learning algorithm proposed by Osugi et al.(2005)explores for new instances using KFF,which does not consider the density of the class distribution.The exploitation phase uses the Simple algorithm which again does not consider the density of samples.3.4DiversitySome active learning algorithms can have added advantage if they take into account the diverse nature of instances in the dataset.The classifier developed will perform well when trained with dataset that has different kinds of samples that represent the entireRanker Computational Density DiversitycomplexityAlgorithm1 x xAlgorithm2 x x xAlgorithm3 x xAlgorithm4 x xAlgorithm5x x xAlgorithm6 x xAlgorithm7Algorithm8x x xTable1:Summary of characteristics of the active learning algorithms in study distribution.The algorithms by Cohn et al.(1995),Tong and Koller(2000),Campbell et al.(2000)and Schohn and Cohn(2000)do not consider the diversity of the samples in the labeled set used for training the classifier.They just select the examples that optimize their criterion, which is minimizing the variance in Cohn et al.’s model and choosing the most unclear example to the classifier in the Simple algorithm.The ALAR algorithm by Iyengar et al.(2000)and the algorithm by Roy and McCallum (2001)also do not select instances based on their diversity.The KFF algorithm by Baram et al.(2003)select those instances that are the farthest from the given set of labeled examples.Intuitively,this picks the instance from the unla-beled which is most dissimilar to the current set of labeled examples used for training the classifier.The probabilistic algorithm by Mitra et al.(2004)selects samples that are far from the current boundary with a confidence factor c.This kind of helps the active learner to pick instances in the dataset that are diverse in nature.The active-learning algorithm by Nguyen and Smeulders(2004),selects diverse samples as it gives priority to samples which are cluster representatives,and each cluster represents a different group of data instances.The algorithm by Osugi et al.(2005),uses KFF in the exploration phase,which considers the diversity of the dataset while selecting the next instance for expert labeling.3.5Close to boundaryInstances lying close to the decision boundary generally contribute to the accuracy of the classifier,as in the case of support vector machines.Hence,those samples lying close to the boundary convey a lot of information regarding the underlying class distribution.In this section we see if the algorithms under study consider the instances lying close to the boundary for expert labeling.The statistical algorithms of Cohn et al.(1995)selects instances that minimize the vari-ance of the learner on the future dataset,and this might be equivalent to picking those instances close to the current decision boundary.The Simple algorithm described queries for instances that the learner is most uncertain about and this leads to choosing samples that are close to the classifier’s decision bound-ary.The ALAR algorithm using3-nn classifier for thefirst task of the determining the degree of uncertainty,minimizes the cumulative error by choosing the instances that are misclas-sified by the classifier algorithm in the second task,given the actual labels are those given by thefirst algorithm.This chooses the samples that are close to the current decision boundary.The active learning algorithm by Roy and McCallum(2001)tries to pick samples that provide maximum reassurance of the model and hence does not pick examples close to the boundary.The KFF algorithm also does not choose samples close to the boundary,it queries for the sample farthest from current labeled training set.The algorithm by Mitra et al.(2004)tries tofind the support vectors of dividing hyper-plane and hence considers the samples lying close to the decision boundary.The active learning with pre-clustering algorithm,tries to minimize the current error of the classifier.This leads to choosing those samples lying on the current classification boundary as they contribute the largest to the current error.The algorithm by Osugi et al.(2005)queries for the samples lying close to the boundary during the exploitation phase,using the Simple active learning algorithm.3.6Far from boundarySome active learning algorithms might query for instances that are far from the current decision boundary,as these examples can help to reassure the model as well as give a chance to explore new instances which might be very informative.The algorithm by Cohn et al.(1995),the Simple algorithm,the ALAR algorithm and theKFF do not query for samples lying far from the current decision boundary.The algorithm proposed by Roy and McCallum(2001)queries for examples that reduce the future generalization error probability.This leads to picking examples that reassure the current model and the samples lying far from the boundary are chosen by the algorithm as the learner is most sure about the labels for those samples.The active learning algorithm by Mitra et al.(2004)queries for samples lying far from the boundary using the confidence factor c,which varies adaptively with iteration.The algorithm proposed by Nguyen and Smeulders(2004)picks instances that lie close to the boundary for expert labeling,not the ones far away as they do not contribute towards the current error of the classifier.The algorithm by Osugi et al.(2005)queries for the samples lying far from the boundary during the exploration phase using the KFF algorithm.3.7Probabilistic or uncertainty of rankerHere we consider whether the ranker used in the active learning algorithm is probabilistic and whether the uncertainty of the ranker is used to query for new instances for expert labeling.Most algorithms studied here in this survey use a probabilistic ranker or uncer-tainty of the ranker to pick the samples for labeling.The active learning algorithms with statistical models by Cohn et al.(1995)uses prob-abilistic measures for determining variance estimates.The Simple active learning algo-rithm use uncertainty sampling to pick the instance that is most unclear to the learner. The ALAR algorithm that uses the ensemble of classifiers generated in the second task is probabilistic and chooses those samples that are misclassified by the learner.In the algo-rithm by Roy and McCallum(2001)uses probabilistic estimates using logistic regression to calculated the expected log-loss.The KFF algorithm does not depend on the probabilistic or uncertainty of the ranker. The algorithm of Mitra et al.(2004)uses the confidence factor c to pick the samples for labeling which is correlated with the selected samples in the labeled training set.The algorithm of Nguyen and Smeulders(2004)uses logistic regression with a proba-bilistic framework and also employs soft cluster membership to choose the sample for labeling.The algorithm by Osugi et al.(2005)considers probability measures for exploring based on feedback.3.8MyopicAn algorithm has a myopic approach when it greedily choose for instances that optimize the criterion at that instance(locally),instead of considering a globally-optimal solution. Most active learning algorithms choose a myopic approach as the learner thinks that the instance it selects for the expert to label,is the last instance that the expert is available for labeling.Most of the algorithms considered in this study adopt a myopic approach as they try to select the instance that optimizes the performance of the current classifier on the future test set.This is a major limitation in case of greedy margin based methods as the algo-rithm never explores if the examples lying far from the current decision boundary have more information to convey regarding the class distribution,which helps the classifier to become more effective.The statistical algorithm of Cohn et al.(1995)queries for the instance that minimizes the expected error of the model by minimizing its variance,which is actually myopic in approach.Similarly,the Simple algorithm by Tong and Koller(2000),Campbell et al. (2000)and Schohn and Cohn(2000)query for the instance that current classifier is most unclear about,at each iteration.Roy and McCallum(2001)also choose the instance that reassures the current model.The KFF algorithm by Baram et al.(2003)also chooses the example that is most different from the current dataset.However,some of these active learning algorithms select multiple instances to request label from the expert at each iteration instead of choosing just one instance.For example, the ALAR algorithm by Iyengar et al.(2000)and the probabilistic algorithm of Mitra et al.(2004)allows the learner to query the expert for labels of multiple samples at each instance.However this does not exactly globally-optimize the problem in hand.The algorithm of Nguyen and Smeulders(2004)gives priority to the cluster represen-tatives for labeling after an initial clustering.It also prioritizes examples from the high density clustersfirst for labeling.This gives the algorithm a kind of approach for global optimization by choosing diverse samples and high density cluster samples initially.But in the later stages the proposed method chooses those instances that contribute the largest to the current error.The algorithm by Osugi et al.(2005)also gives importance to exploring the dataset with a probability p,apart from just optimizing the current criterion.A high value of p encour-ages exploration and is maintained with a high value if the current hypothesis changes. Otherwise it is updated to reduce the probability of exploration at the next step.The value of p has an upper and lower bound,and hence there is always a chance of exploring and exploiting.Close to Far from Probabilistic/Myopicboundary boundary uncertaintyof rankerAlgorithm1 xAlgorithm2 xAlgorithm3 x not clearAlgorithm4xAlgorithm5x x xAlgorithm6 not clearAlgorithm7 x xAlgorithm8 xTable2:Summary of characteristics of the active learning algorithms in studyAlgorithm Authors Name1Cohn et al.(2000)Active learning with statistical models2Tong and Koller(2000)Simple MarginCampbell et al.(2000)Query learning with large margin classifiersSchohn and Cohn(2000)Less is More:Active learning with supportvector machines3Iyengar et al.(2000)Active learning with adaptive resampling4Roy and McCallum(2001)Active learning with Samplingestimation of error reduction5Baram et al.(2003)Kernel Farthest First6Mitra et al.(2004)Probabilistic active support vectorlearning algorithm7Nguyen and Smeulders(2004)Active learning with pre-clustering8Osugi et al.(2005)Balancing exploration and exploitationalgorithm for active learningTable3:Key to Table1&24ConclusionActive learning enables the application of machine learning methods to problems where it is difficult or expensive to acquire expert labels.Empirical results presented in the studied research papers indicate that active-learning based classifiers perform better than passive ones.In this paper we have presented a survey of several state-of-the-art active learning algorithms as well as the most popular ones.A detailed analysis of each algorithm has been made based on the characteristics of each algorithm,which gives an insight into the features each active learning algorithm considers while querying for instances for expert labeling.References[1]A NGLUIN,D.Queries and concept learning.Mach.Learn.2,4(1988),319–342.[2]B ARAM,Y.,E L-Y ANIV,R.,AND L UZ,K.Online choice of active learning algo-rithms,2003.[3]C AMPBELL,C.,C RISTIANINI,N.,AND S MOLA,A.Query learning with largemargin classifiers.In Proc.17th International Conf.on Machine Learning(2000), Morgan Kaufmann,San Francisco,CA,pp.111–118.[4]C OHN,D.A.,G HAHRAMANI,Z.,AND J ORDAN,M.I.Active learning withstatistical models.In Advances in Neural Information Processing Systems(1995),G.Tesauro,D.Touretzky,and T.Leen,Eds.,vol.7,The MIT Press,pp.705–712.[5]F REUND,Y.,S EUNG,H.S.,S HAMIR,E.,AND T ISHBY,N.Selective samplingusing the query by committee algorithm.Machine Learning28,2-3(1997),133–168.[6]I YENGAR,V.S.,A PTE,C.,AND Z HANG,T.Active learning using adaptive resam-pling.In KDD’00:Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining(New York,NY,USA,2000),ACM Press, pp.91–98.[7]L EWIS,D.D.,AND G ALE,W.A.A sequential algorithm for training text clas-sifiers.In SIGIR’94:Proceedings of the17th annual international ACM SIGIR conference on Research and development in information retrieval(New York,NY, USA,1994),Springer-Verlag New York,Inc.,pp.3–12.[8]M ITRA,P.,M URTHY,C.,AND P AL,S.K.A probabilistic active support vectorlearning algorithm.IEEE Transactions on Pattern Analysis and Machine Intelli-gence26,3(2004),413–418.[9]N GUYEN,H.T.,AND S MEULDERS,A.Active learning using pre-clustering.InICML’04:Proceedings of the twenty-first international conference on Machine learning(New York,NY,USA,2004),ACM Press,p.79.[10]O SUGI,T.,K UN,D.,AND S COTT,S.Balancing exploration and exploitation:Anew algorithm for active machine learning.In ICDM’05:Proceedings of the Fifth IEEE International Conference on Data Mining(Washington,DC,USA,2005), IEEE Computer Society,pp.330–337.[11]R OY,N.,AND M C C ALLUM,A.Toward optimal active learning through samplingestimation of error reduction.In Proc.18th International Conf.on Machine Learn-ing(2001),Morgan Kaufmann,San Francisco,CA,pp.441–448.[12]S CHOHN,G.,AND C OHN,D.Less is more:Active learning with support vectormachines.In Proc.17th International Conf.on Machine Learning(2000),Morgan Kaufmann,San Francisco,CA,pp.839–846.[13]S EUNG,H.S.,O PPER,M.,AND S OMPOLINSKY,H.Query by committee.InComputational Learning Theory(1992),pp.287–294.[14]T ONG,S.,AND K OLLER,D.Support vector machine active learning with applica-tions to text classification.In Proceedings of ICML-00,17th International Confer-ence on Machine Learning(Stanford,US,2000),ngley,Ed.,Morgan Kaufmann Publishers,San Francisco,US,pp.999–1006.。
Naive-Bayes
– 先验概率:
– 条件概率: – 联合概率:
P( X )
P( X1 |X2 ), P(X2 | X1 ) X ( X1 , X2 ), P( X ) P(X1 ,X2 ) P( X2 | X1 ) P( X2 ), P( X1 | X2 ) P( X1 ), P(X1 ,X2 ) P( X1 )P( X2 )
14
相关问题
• 输入属性值是连续的
– 对于一个属性有无数个值
– 基于正态分布的条件概率模型
( X j ji )2 ˆ ( X |C c ) P exp j i 2 2 ji 2 ji ji : mean (avearage) of attribute values X j of examples for which C ci 1
•
基于MAP规则的生成分类器
– 使用贝叶斯法则将上式转换为为后验概率的形式
P( X x |C ci )P(C ci ) P(C ci |X x) P( X x) P( X x |C ci )P(C ci ) fo r i 1,2 , , L
– 然后再使用MAP规则
– 学习阶段: 给定一个训练集合S,
Fo r eac h targ et value o f ci (ci c1 , , c L ) ˆ (C c ) estimate P(C c ) with examples in S; P i i Fo r everyattribute value x jk o f eac hattribute X j ( j 1, , n; k 1, , N j ) ˆ ( X x |C c ) estimate P( X x |C c ) with examples in S; P j i j i jk jk
人工智能算法工程师：深度学习与神经网络算法培训ppt
TensorFlow框架特点及使用方法
特点
TensorFlow是一个开源的深度学习框架，具有高度的灵活性 和可扩展性。它支持分布式训练，能够在多个GPU和CPU上 加速训练过程。TensorFlow还提供了丰富的API和工具，方 便用户进行模型开发和调试。
使用方法
使用TensorFlow进行深度学习需要先安装TensorFlow库，然 后通过编写Python代码来定义模型、加载数据、进行训练和 评估等操作。TensorFlow提供了高级的API，如Keras，可以 简化模型开发和训练过程。
PyTorch框架特点及使用方法
特点
PyTorch是一个轻量级的深度学习框架，具有简单易用的特点。它支持动态计算 图，使得模型开发和调试更加灵活。PyTorch还提供了GPU加速和分布式训练功 能，能够提高训练速度。
使用方法
使用PyTorch进行深度学习需要先安装PyTorch库，然后通过编写Python代码来 定义模型、加载数据、进行训练和评估等操作。PyTorch提供了高级的API，如 torch.nn和torch.optim，可以简化模型开发和训练过程。
模型可解释性不足
深度学习模型的可解释性一直是研究 难点。未来需要加强模型可解释性的 研究，以更好地理解模型的工作原理 。
THANKS。
将有更多创新方法被提出。
面临的挑战与解决方案探讨
数据隐私与安全
计算资源需求大
随着深度学习应用的广泛使用，数据 隐私和安全问题日益突出。需要采取 数据脱敏、加密等技术手段来保护用 户隐私。
深度学习模型的训练和推理需要大量 的计算资源，如高性能计算机、GPU 等。需要进一步优化算法和模型结构 ，以降低计算资源需求。
人工智能算法工程师：深度学习 与神经网络算法培训
现代汉语词类划分标准
研究目的和方法
本研究旨在进一步明确现代汉语词类的划分标准，为汉语语言学研究提供更为准确和可靠的基础。
研究方法主要包括文献综述、语言实例分析和实证研究等，通过综合分析，提出一种更为合理和科学的现代汉语词类划分标 准。
研究不足与展望
最后，对于一些新兴词语和网络 语言的词类划分问题，仍需进一 步探讨和研究。随着社会的发展 和语言的演变，新的词汇不断涌 现，如何准确划分这些词语的词 类属性，是当前研究的重要课题 之一。
未来对于现代汉语词类划分标准 的研究，需要进一步拓展和深化 。可以从以下几个方面进行展望
首先，需要进一步加强对于特殊 词语的词类归属问题的研究，以 完善现代汉语词类划分的理论体 系。
研究不足与展望
其次，在以“功能”为标准进行词类划分时，需要更加注重 对于功能变化较为灵活的词语的研究，以准确界定其词类属 性。
最后，需要关注新兴词语和网络语言的词类划分问题，为现 代汉语语言学的发展提供更加全面和准确的理论支持。
THANKS
感谢观看
VS
详细描述
词汇关系是指词语之间的语义关系和组合 方式。根据词汇关系对现代汉语词类进行 划分，可以更深入地理解词语的语义属性 和组合特点。例如，近义词是指意义相近 的词，它们通常可以互相替换，因此可以 根据近义词之间的词汇关系将其划分为一 类。
基于语义相似度的词类划分
总结词
根据词的语义相似度和概念关系来划分词类
02 现代汉语词类划 分的历史与现状
古代汉语词类划分的起源
古代汉语词类划分起源可以追溯到先秦时期，当时的语言研究者和学者开始对汉 语词类进行初步的划分。
CDA_LEVEL_2试题及答案
CDA LEVELⅡ建模分析师_模拟题：一、单项选择题（每小题0.5分，共30分）1、答案（D）在使用历史数据构造训练集（Train）集、验证（Validation）集和检验（Test）时，以下哪个样本量分配方案比较适合？A.训练50%，验证0%，检验50%B.训练100%，验证0%，检验0%C.训练0%，验证100%，检验0%D.训练60%，验证30%，检验10%2、答案(A)一个累积提升度曲线，当深度（Depth）等于0.1时，提升度为(Lift)为3.14，以下哪个解释正确?A.根据模型预测，从最高概率到最低概率排序后，最高的前10%中发生事件的数量比随机抽样的响应率高3.14B.选预测响应概率大于10%的样本，其发生事件的数量比随机抽样的响应率高3.14C.根据模型预测，从最高概率到最低概率排序后，最高的前10%中预测的精确度比随机抽样高3.14D.选预测响应概率大于10%的样本，其预测的精确度比随机抽样高3.143、答案（C）在使用历史数据构造训练（Train）集、验证（Validation）集和检验（Test）集时，训练数据集的作用在于A.用于对模型的效果进行无偏的评估B.用于比较不同模型的预测准确度C.用于构造预测模型D.用于选择模型4、答案（D）在对历史数据集进行分区之前进行数据清洗（缺失值填补等）的缺点是什么？A.增加了填补缺失值的时间B.加大了处理的难度C.无法针对分区后各个数据集的特征分别做数据清洗D.无法对不同数据清理的方法进行比较，以选择最优方法5、答案（C）关于数据清洗（缺失值、异常值），以下哪个叙述是正确的？A.运用验证数据集中变量的统计量对训练集中的变量进行数据清洗B.运用验证数据集中变量的统计量对验证集中的变量进行数据清洗C.运用训练数据集中变量的统计量对验证集中的变量进行数据清洗D.以上均不对6、答案（B）当一个连续变量的缺失值占比在85%左右时，以下哪种方式最合理A.直接使用该变量，不填补缺失值B.根据是否缺失，生成指示变量，仅使用指示变量作为解释变量C.使用多重查补的方法进行缺失值填补D.使用中位数进行缺失值填补7、答案（B）构造二分类模型时，在变量粗筛阶段，以下哪个方法最适合对分类变量进行粗筛A.相关系数B.卡方检验C.方差分析D.T检验8、答案（A）以下哪个方法可以剔除多变量情况下的离群观测A.变量中心标准化后的快速聚类法B.变量取百分位秩之后的快速聚类法C.变量取最大最小秩化后的快速聚类法D.变量取Turkey转换后的快速聚类法9、答案（C）以下哪种变量筛选方法需要同时设置进出模型的变量显著度阀值A.向前逐步法B.向后逐步法C.逐步法D.全子集法10、答案（A）以下哪个指标不能用于线性回归中的模型比较：A.R方B.调整R方C.AICD.BIC11、[答案B.]将复杂的地址简化成北、中、南、东四区，是在进行？A.数据正规化(Normalization)B.数据一般化(Generalization)C.数据离散化(Discretization)D.数据整合(Integration)12、【答案（A）】当类神经网络无隐藏层，输出层个数只有一个的时候，倒传递神经网络会变形成为？A.罗吉斯回归B.线性回归C.贝氏网络D.时间序列13、[答案B.]请问Apriori算法是用何者做项目集(Itemset)的筛选?A.最小信赖度(Minimum Confidence)B.最小支持度(Minimum Support)C.交易编号(Transaction ID)D.购买数量14、[答案B.]有一条关联规则为A→B，此规则的信心水平(confidence)为60%，则代表：A.买B商品的顾客中，有60%的顾客会同时购买AB.买A商品的顾客中，有60%的顾客会同时购买BC.同时购买A,B两商品的顾客，占所有顾客的60%D.两商品A,B在交易数据库中同时被购买的机率为60%15、【答案（B）】下表为一交易数据库，请问A→C的支持度(Support)为:A.75%B.50%C.100%D.66.6%TID Items Bought1A,B,C2A,C3A,D4B,E,F16、【答案（D）】下表为一交易数据库，请问A→C的信赖度(Confidence)为:A.75%B.50%C.100%D.66.6%TID Items Bought1A,B,C2A,C3A,D4B,E,F17、[答案D.]倒传递类神经网络的训练顺序为何？(A:调整权重;B:计算误差值;C:利用随机的权重产生输出的结果)A.BCAB.CABC.BACD.CBA18、[答案C.]在类神经网络中计算误差值的目的为何？A.调整隐藏层个数B.调整输入值C.调整权重(Weight)D.调整真实值19、[答案A.]以下何者为Apriori算法所探勘出来的结果?A.买计算机同时会购买相关软件B.买打印机后过一个月会买墨水夹C.买计算机所获得的利益D.以上皆非20、[答案D.]如何利用「体重」以简单贝式分类(Naive Bayes)预测「性别」？A.选取另一条件属性B.无法预测C.将体重正规化为0~1之间D.将体重离散化21、[答案B.]Naive Bayes是属于数据挖掘中的什么方法？A.分群B.分类C.时间序列D.关联规则22、[答案B.]简单贝式分类(Naive Bayes)可以用来预测何种数据型态？A.数值B.类别C.时间D.以上皆是23、[答案B.]如何以类神经网络仿真罗吉斯回归(Logistic Regression)？A.输入层节点个数设定为3B.隐藏层节点个数设定为0C.输出层节点个数设定为3D.隐藏层节点个数设定为124、[答案B.]请问以下何者属于时间序列的问题？A.信用卡发卡银行侦测潜在的卡奴B.基金经理人针对个股做出未来价格预测C.电信公司将人户区分为数个群体D.以上皆是25、[答案D.]小王是一个股市投资人，手上持有某公司股票，且已知该股过去历史数据如下表所示，今天为预测2/6的股价而计算该股3日移动平均，请问最近的3日移动平均值为多少？日期股价2/1102/2122/3132/4162/519A.11B.13C.14D.1626、[答案C.]下列哪种分类算法的训练结果最难以被解释？A.Naive BayesB.Logistic RegressionC.Neural NetworkD.Decision Tree27、[答案B.]数据遗缺(Null Value)处理方法可分为人工填补法及自动填补法，下列哪种自动填补法可得到较准确的结果？A.填入一个通用的常数值，例如填入"未知/Unknown"B.把填遗缺值的问题当作是分类或预测的问题C.填入该属性的整体平均值D.填入该属性的整体中位数二、多项选择题1、（AB）对于决策类模型、以下哪些统计量用于评价最合适？A.错分类率B.利润C.ROC指标D.SBC对于估计类模型、以下哪些统计量用于评价最合适？A.错分类率B.极大似然数C.ROC统计量D.SBC3、（AB）以下哪个变量转换不会改变变量原有的分布形式A.中心标准化B.极差标准化C.TURKEY打分D.百分位秩4、（AB）连续变量转换时，选取百分位秩而不选用最大最小秩的原因A.避免模型在使用时，值域发生明显变化B.避免输入变量值域变化对模型预测效果的影响C.避免输入变量的异常值影响D.是转换后的变量更接近正态分布构造二分类模型时，在变量粗筛阶段，以下哪两个方法最适合对连续变量进行粗筛A.皮尔森（Pearson）相关系数B.思皮尔曼（SPEARMAN）相关系数C.Hoeffding’s D相关指标D.余弦相关指标6、（CD）常见的用于预测Y为分类变量的回归方法有A.伽玛回归B.泊松回归C.Logistic回归D.Probit回归7、(A,B,C)请问以下个案何者属于时间序列分析的范畴？A.透过台湾股票指数过去十年走势预测其未来落点B.透过美国股票指数走势变动以分析其与台股指数的连动因果C.透过突发事件前后的股票指数走势变动来探讨该事件的影响D.分析投资人对不同股票的喜好程度8、(A,B,C)下表为一事务数据库，若最小支持度(Minimum Support )=50%，则以下哪些是长度为2的频繁项目集(Frequent Itemset)？A.BEB.ACC.BCD.AB 9.(B,C,D)下列对C4.5算法的描述，何者为真？A.每个节点的分支度只能为2B.使用gain ratio 作为节点分割的依据C.可以处理数值型态的字段D.可以处理空值的字段10.(A,B,D)下列哪个应用可以使用决策树来建模？TID Items Bought1A,C,D 2B,C,E 3A,B,C,E 4B,EA.预测申办信用卡的新客户是否将来会变成卡奴B.银行针对特定族群做人寿保险的推销C.找出购物篮里商品购买间的关联D.根据生活作息推断该病人得癌症的机率11.(B,C)小王是一个股市投资人，手上持有A、B、C、D、E五只股票，请问以下何者不属于时间序列的问题？A.透过A只股票过去一年来的股价走势，预测明天A只股票的开盘价格B.将A、B、C、D、E五只股票区分为赚钱与赔钱两个类别C.将A、B、C、D、E五只股票区分为甲、乙、丙三个群体D.透过A,C,D三只股票过去一年来的走势，预测明天A只股票的开盘价格12.(A,C,D)下列何者是类神经网络的缺点？A.无法得知最佳解B.模型准确度低C.知识结构是隐性的，缺乏解释能力D.训练模型的时间长13.(A,B)请问要符合什么条件才可被称为关联规则?A.最小支持度(Minimum Support)B.最小信赖度(Minimum Confidence)C.最大规则数(Maximum Rule Number)D.以上皆非三、内容相关题根据相同的背景材料回答若干道题目，每道题的答案个数不固定。
文本分类过程ppt课件
朴素贝叶斯分类器(Naive Bayes Classifier,或 NBC)，是基于贝叶斯定理与特征条件独立假设的分类方 法。NBC模型所需估计的参数很少，对缺失数据不敏感。
K 近邻算法（K-Nearest Neighbor,KNN），核心思想是如果一个样本在特征空间中的k个最相邻的样 本中的大多数属于某一个类别，则该样本也属于这个类别，并具有这个类别上样本的特性。由于 KNN方法主要靠周围有限的邻近的样本，因此对于类域的交叉或重叠较多的待分样本集来说，KNN 方法较其他方法更为适合。
01 文本分类概述
文本分类技术（Text Categorization，TC）作为组织和管理文本信 息的有效手段，主要任务是自动分类无标签文档到预定的类别集合中。
文本可以是媒体新闻、科技、报告、电子邮件、网页、书籍或像微博 一样的一段语料。由于类别时事先定色社 会主义 思想和 党的十 九大精 神,贯彻 全国教 育大会 精神,充 分发挥 中小学 图书室 育人功 能
特征个数越多，分析特征、训练模型所需的时间就越长。 特征个数越多，容易引起“维度灾难”，模型也会越复杂，其推广能力会下降。 特征选择能剔除不相关(irrelevant)或冗余(redundant )的特征，从而达到减少特征个数
，提高模型精确度，减少运行时间的目的。另一方面，选取出真正相关的特征简化了 模型，使研究人员易于理解数据产生的过程。
在一个文本中出现次数很多的单词，在另一个同类文本中出现的也会很多，反之亦然，所以将 TF(词频)作为测度;
一个词条出现的文本频数越小，它区别不同类别的能力就越大，故引入了 IDF(逆文本频数)的概念。
为深入学习习近平新时代中国特色社 会主义 思想和 党的十 九大精 神,贯彻 全国教 育大会 精神,充 分发挥 中小学 图书室 育人功 能
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Normal distribution:
P( xi | j ) ( xi ij )2 exp 2 2 2 2 ij ij 1
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© Deng Cai, College of Computer Science, Zhejiang University Introduction (Introduction to Artificial Intelligence, Fall 2011)
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Naïve Bayes (Summary)
Nearest Neighbor Classifiers
Basic idea:
© Deng Cai, College of Computer Science, Zhejiang University Introduction (Introduction to Artificial Intelligence, Fall 2011)
Since P(X|No)P(No) > P(X|Yes)P(Yes) Therefore P(No|X) > P(Yes|X)
=> Class = No
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Example of Naï ve Bayes Classifier
Name Give Birth Can Fly Live in Water Have Legs Class
Introduction to Artificial Intelligence
Deng Cai (蔡登)
College of Computer Science Zhejiang University dengcai@
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Prior
State of nature, a priori (prior) probability
P( Income 120 | No)
(120 110) 2 exp 0.0072 2(2975) 2 (54.54) 1
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Example of Naï ve Bayes Classifier
Given a Test Record:
X (Refund No, Married, Income 120K)
Yes No No Yes No No Yes No No No
2 3 4 5 6 7 8 9 10
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One for each (xi, i) pair For (Income, Class=No): If Class=No
sample mean = 110 sample variance = 2975
Decision rule with only the prior information
Decide 1 if P(1) > P(2), otherwise decide 2
© Deng Cai, College of Computer Science, Zhejiang University Introduction (Introduction to Artificial Intelligence, Fall 2011)
A: attributes M: mammals
N: non-mammals
6 6 2 2 P ( A | M ) 0.06 7 7 7 7 1 10 3 4 P ( A | N ) 0.0042 13 13 13 13 7 P ( A | M ) P ( M ) 0.06 0.021 20 13 P ( A | N ) P ( N ) 0.004 0.0027 20
P(Refund=Yes|No) = 3/7 P(Refund=No|No) = 4/7 P(Refund=Yes|Yes) = 0 P(Refund=No|Yes) = 1 P(Marital Status=Single|No) = 2/7 P(Marital Status=Divorced|No)=1/7 P(Marital Status=Married|No) = 4/7 P(Marital Status=Single|Yes) = 2/7 P(Marital Status=Divorced|Yes)=1/7 P(Marital Status=Married|Yes) = 0 For taxable income: If class=No: sample mean=110 sample variance=2975 If class=Yes: sample mean=90 sample variance=25
Naïve Bayes Classifier
How to Estimate Probabilities from Data? s al al
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Tid
© Deng Cai, College of Computer Science, Zhejiang University Introduction (Introduction to Artificial Intelligence, Fall 2011)
How to Estimate How tto from Data? s in Probabilities go eg e t nt as ca ca co cl
Tid Refund Marital Status Single Married Single Married Divorced Married Divorced Single Married Single Taxable Income 125K 100K 70K 120K 95K 60K 220K 85K 75K 90K Evade No No No No Yes No No Yes No Yes
© Deng Cai, College of Computer Science, Zhejiang University Introduction (Introduction to Artificial Intelligence, Fall 2011)
human python salmon whale frog komodo bat pigeon cat leopard shark turtle penguin porcupine eel salamander gila monster platypus owl dolphin eagle
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Refund
Marital Status Single Married Single Married Divorced Married Divorced Single Married Single
Taxable Income 125K 100K 70K 120K 95K 60K 220K 85K 75K 90K
Evade No No No No Yes No No Yes No Yes
1 2 3 4 5 6 7 8 9 10
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Yes No No Yes No No Yes No No No
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© Deng Cai, College of Computer Science, Zhejiang University Introduction (Introduction to Artificial Intelligence, Fall 2011)
© Deng Cai, College of Computer Science, Zhejiang University Introduction (Introduction to Artificial Intelligence, Fall 2011)
Random variable (State of nature is unpredictable) The catch of salmon and sea bass is equiprobable P(1) = P(2) (uniform priors) P(1) + P( 2) = 1 (exclusivity and exhaustivity) Reflects our prior knowledge about how likely we are to observe a sea bass or salmon
© Deng Cai, College of Computer Science, Zhejiang University Introduction (Introduction to Artificial Intelligence, Fall 2011)
naive Bayes Classifier:
Likelihood
© Deng Cai, College of Computer Science, Zhejiang University Introduction (Introduction to Artificial Intelligence, Fall 2011)
Posterior
© Deng Cai, College of Computer Science, Zhejiang University Introduction (Introduction to Artificial Intelligence, Fall 2011)
P(X|Class=No) = P(Refund=No|Class=No) P(Married| Class=No) P(Income=120K| Class=No) = 4/7 4/7 0.0072 = 0.0024
P(X|Class=Yes) = P(Refund=No| Class=Yes) P(Married| Class=Yes) P(Income=120K| Class=Yes) = 1 0 1.2 10-9 = 0