Concept-Based Data Classification in Relational Databases
关于分类的英语作文
关于分类的英语作文Classification is a fundamental concept in our daily lives. It is the process of organizing and arranging items into groups based on their similarities and differences. Classification helps us make sense of the world around us and allows us to understand and interpret information more effectively.There are many different ways to classify items, and each method has its own set of rules and criteria. For example, items can be classified based on their physical attributes, such as size, shape, color, or texture. They can also be classified based on their function or purpose, such as tools, clothing, or food. In addition, items can be classified based on their relationships to other items, such as family trees or organizational charts.One of the most common methods of classification is the use of categories. Categories are groups of items that share similar characteristics or properties. For example,animals can be classified into categories such as mammals, birds, reptiles, and amphibians. Similarly, plants can be classified into categories such as trees, shrubs, flowers, and grasses. Categories help us to organize and understand the vast diversity of the natural world.Another method of classification is the use of hierarchies. Hierarchies are systems of classification in which items are organized into levels of importance or complexity. For example, in a biological hierarchy, living organisms are classified into domains, kingdoms, phyla, classes, orders, families, genera, and species. Hierarchies help us to understand the relationships between different groups of items and how they are related to one another.Classification is also important in the field of science. Scientists use classification to organize and categorize the vast amount of information that they gather through their research. For example, in the field of biology, scientists classify living organisms intodifferent groups based on their evolutionary relationships and physical characteristics. This helps them to betterunderstand the diversity of life on Earth and how different species are related to one another.In addition to its scientific applications, classification is also important in everyday life. We use classification to organize our belongings, such as clothes, books, and household items. We also use classification to organize information, such as files, documents, and data. By classifying items, we can easily locate and retrieve them when needed, which helps us to stay organized and efficient.In conclusion, classification is a fundamental concept that helps us make sense of the world around us. It allows us to organize and understand the vast diversity of items and information that we encounter on a daily basis. Whether it is in the field of science or in our everyday lives, classification plays a crucial role in helping us to interpret and navigate the world.。
英文classification作文
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文档下载后可定制随意修改，请根据实际需要进行相应的调整和使用，谢谢!并且，本店铺为大家提供各种各样类型的实用资料，如教育随笔、日记赏析、句子摘抄、古诗大全、经典美文、话题作文、工作总结、词语解析、文案摘录、其他资料等等，如想了解不同资料格式和写法，敬请关注!Download tips: This document is carefully compiled by theeditor. I hope that after you download them,they can help yousolve practical problems. The document can be customized andmodified after downloading,please adjust and use it according toactual needs, thank you!In addition, our shop provides you with various types ofpractical materials,such as educational essays, diaryappreciation,sentence excerpts,ancient poems,classic articles,topic composition,work summary,word parsing,copyexcerpts,other materials and so on,want to know different data formats andwriting methods,please pay attention!Classification is a way of organizing things based on their similarities and differences. It helps us make sense of the world around us by grouping similar things together and separating different things. For example, we can classify animals into different groups based on whether they have fur, feathers, or scales.In science, classification is used to organize living organisms into different categories based on their characteristics. This helps scientists understand the diversity of life on Earth and how different species are related to each other. It also allows us to study and compare different species to learn more about their behavior, ecology, and evolution.In everyday life, we use classification to organize and categorize things to make it easier to find and understand them. For example, we classify books in a library based on their genre, author, or topic. We also classify food intodifferent groups such as fruits, vegetables, grains, and proteins to help us make healthy meal choices.Classification is also used in technology to organize and categorize data. For example, search engines use classification algorithms to categorize and rank web pages based on their relevance to a user's search query. This helps users find the information they are looking for more quickly and easily.Overall, classification is a fundamental concept that helps us make sense of the world and organize information in a meaningful way. It allows us to understand the relationships between different things and make informed decisions based on these relationships. Whether in science, everyday life, or technology, classification plays acrucial role in how we understand and interact with the world around us.。
面向垃圾图像分类的残差语义强化网络
第41卷 第6期吉林大学学报(信息科学版)Vol.41 No.62023年11月Journal of Jilin University (Information Science Edition)Nov.2023文章编号:1671⁃5896(2023)06⁃1030⁃11面向垃圾图像分类的残差语义强化网络收稿日期:2023⁃04⁃25基金项目:国家自然科学基金资助项目(62006209)作者简介:苏雯(1992 ),女,陕西汉中人,浙江理工大学讲师,博士,主要从事语义分割㊁单目深度估计和三维场景理解等研究,(Tel)86⁃137****6801(E⁃mail)wensu@㊂苏 雯,徐鑫林,胡宇超,黄博涵,周佩廷(浙江理工大学信息科学与工程学院,杭州310018)摘要:为更好地保护生态环境并提高可回收垃圾的经济价值,针对现有的垃圾识别方法面临的分类背景复杂㊁垃圾目标形态变化多样等问题,提出一种面向垃圾图像分类的残差语义强化网络,能从复杂背景中剥离前景语义目标㊂该网络以骨干残差网络为基础,利用视觉概念采样㊁推理以及调制模块实现视觉语义的提取,并通过注意力模块消除语义层次和空间分辨率与视觉概念特征的差距,从而对垃圾目标形态变化更加具有鲁棒性㊂通过在Kaggle 开源的12分类垃圾数据集及TrashNet 数据集上进行实验,结果表明,相较于骨干网络ResNeXt⁃50和其他深层网络,该算法均取得了性能的提升,在垃圾图像分类任务上有较好表现㊂关键词:模式识别与智能系统;垃圾分类;视觉概念;视觉采样;概念推理;注意力机制中图分类号:TP391文献标志码:ANetwork of Residual Semantic Enhancement for Garbage Image ClassificationSU Wen,XU Xinlin,HU Yuchao,HUANG Bohan,ZHOU Peiting(School of Information Science and Engineering,Zhejiang Sci⁃Tech University,Hangzhou 310018,China)Abstract :In order to better protect the ecological environment and increase the economic value of recyclable waste,to solve the problems faced by the existing garbage identification methods,such as the complex classification background and the variety of garbage target forms,a residual semantic enhancement network for garbage image classification is proposed,which can strip foreground semantic objects from complex backgrounds.Based on the backbone residual network,the network uses visual concept sampling,inference and modulation modules to achieve visual semantic extraction,and eliminates the gap between semantic level and spatial resolution and visual concept features through the attention module,so as to be more robust to the morphologicalchanges of garbage targets.Through experiments on the Kaggle open source 12classified garbage dataset and TrashNet dataset,the results show that compared with the backbone network ResNeXt⁃50and some other deep networks,the proposed algorithms have improved performance and have good performance in garbage image classification.Key words :pattern recognition and intelligent system;garbage classification;visual concept;visual sampling;concept reasoning;attention mechanism 0 引 言随着社会生活的不断丰富,人类产生的生活垃圾数量也在急剧增加㊂为保护生态环境,尽可能提高可回收利用垃圾的经济价值,垃圾分类显得尤为重要㊂早在2017年,国家发改委㊁住房城乡建设部便推出‘生活垃圾分类制度实施方案“,以推动垃圾分类的实施和部署㊂ 十四五”规划中对生活垃圾分类和处理设施覆盖水平也提出了明确目标㊂然而垃圾分类在许多地区成效并不明显,而且有些地区还依赖于人力分拣㊂由于手工垃圾分类不仅需要投入大量的劳动力,并且分类的效果也不尽人意,因此基于视觉的垃圾自动分类方法引起众多研究者的兴趣㊂随着AlexNet [1]的问世,基于卷积神经网络(CNN:Convolution Neural Network)和深度学习的分类方法迅速发展,人们对通过卷积神经网络进行图像分类任务的研究不断深入㊂2016年,He 等[2]提出残差网络(ResNet:Residual Network),该网络有效提升了图像分类算法的性能㊂以此为基础,深度学习和CNN 算法在垃圾图像分类领域取得了较好进展㊂李妍[3]通过中值滤波法对图像进行预处理,有效地降低了图像噪声,再通过训练融合注意力模块和特征融合模块的残差网络,获得比较精确的垃圾分类结果㊂王超等[4]首先通过二维Gamma 函数对图像进行光照矫正预处理,再引入带泄露值的激活函数(Leaky ReLU),并将批标准化函数(Batch Normalize)放在卷积操作前,优化ResNet⁃50的网络结构实现垃圾快速有效分类㊂张丽艳等[5]选用包含1000个图像类别且手动注释完成的ImageNet 数据集作为源域对目标数据集进行迁移学习,将其他层参数结构冻结,只改变全连接层的结构参数对模型进行训练,提高ResNet⁃50垃圾分类效率㊂王玉等[6]利用深度残差收缩网络(DRSN:Deep Residual Shrinkage Network),通过改进ResNet 网络,将压缩与激励网络(SENet:Squeeze⁃and⁃Excitation Networks)和软阈值化操作并入残差网络结构中,消除网络层数和噪声对实验结果的负面影响,使垃圾分类准确率得到一定提升㊂徐明明等[7]将残差网络VGG⁃16进行改进,在原有网络基础上加入了双通道注意力机制,在塑料垃圾分类上取得了较好结果㊂综上可知,基于深度学习算法在垃圾分类方面有较好前景,并引起研究者们的深入研究㊂但上述基于深度学习的垃圾分类方法在其识别过程中存在背景较为复杂㊁目标形态变化和光照条件影响问题㊂一方面背景较为复杂的图片在进行图片预处理时可能会出现垃圾主体和背景混淆的情况,导致垃圾主体被当成噪声去除,另一方面复杂的背景在训练或测试过程中可能会影响视觉特征提取;垃圾目标形态变化多样,同一物品在不同形态下特征区别很大,例如,打碎的生鸡蛋和切碎的熟鸡蛋,它们在训练㊁测试过程中提取出的低层视觉特征关联不大,但在语义关系上,其归属于同一个语义类别;不同场景光照变化,光照条件较好的情况下照片的特征会更加突出,而光照条件不好的情况下照片的特征则会受到一定的削弱,例如产生阴影,遮挡等,影响图像识别的结果㊂针对上述问题,笔者利用一种面向垃圾图像分类的残差语义强化网络实现垃圾分类识别㊂该方法以残差神经网络为骨干网络,采用视觉概念采样模块从概念特征图中导出视觉概念状态㊂通过视觉概念推理模块模拟各个处于独立分支的视觉概念状态之间的相互关联,并利用这种联系进行相应视觉概念更新㊂视觉概念调制模块将对更新后的视觉概念进行维数调制,使其传播到网络主流中㊂由于不同层级的特征之间在语义层次和空间分辨率上存在差距,直接组合不同层级的特征可能不太有效㊂在许多现有方法中注意力机制对图像特征提取有较好效果[8⁃13],故笔者将骨干残差网络的低层块输出传到卷积块注意力模块(CBAM:Convolutional Block Attention Module)[14],该模块提取有利于空间轮廓定位,边缘捕捉的底层细节,滤除在空间和通道上的冗余信息㊂同时许多方法表明特征的融合机制能有效提升图像分类准确率[15⁃16],王敏[17]将不同卷积层的图像特征进行加权融合,在生活垃圾图像分类方面取得了较好结果㊂故笔者也将CBAM 的输出特征再与提取出的视觉概念特征进行连接融合㊂该方法通过上述3个模块对输入垃圾图像中目标视觉概念的语义提取,能精确推理出图像中的垃圾目标㊂相比其他垃圾分类方法,笔者提出的方法更多的依赖于实例语义的推理而非图像视觉线索实现目标分类,从而对图像中容易引起低层视觉表征错误的因素例如复杂背景,目标形态变化,光照条件影响等有更好的鲁棒性,可有效提高生活垃圾识别准确率㊂同时在开源数据集上的大量实验表明,相比基准ResNeXt 残差网络和其他垃圾分类方法,笔者在只增加较少参数量的前提下有效提高了模型性能㊂此外,由于缺乏实际生活场景下的开源垃圾分类数据集,垃圾分类方法在统一数据集上的表现难以衡量,笔者采集并标定了生活中常见的厨余垃圾数据集(RKG Dataset:Realistic Kitchen Garbage Dataset)㊂1 提出的方法传统的垃圾图像分类技术首先提取垃圾图像中的内容特征,如颜色㊁纹理特征提取等,再使用机器1301第6期苏雯,等:面向垃圾图像分类的残差语义强化网络学习的分类算法或通过相似性匹配等方法建立图像特征和图像类别之间的关联,从而实现垃圾自动分类㊂如万华林等[18]通过支持向量机提取视觉特征,实现图像的语义分类㊂但传统的手工特征尚不足以完整表达图像的高级语义,分类准确率低㊂近年来,深度学习大受欢迎,RCNN [19](Region⁃CNN)㊁SSD [20](Single Shot MultiBox Detector)㊁YOLO [21](You Only Look Once)㊁ResNet [2]㊁SqueezeNet [22]等深度神经网络已被广泛应用于目标识别,更延伸出迁移学习方法,这些方法在图像分类问题上取得了更好的应用效果㊂但这些方法大多适用于自然图像分类,对垃圾分类场景的适应性不强㊂少数针对垃圾分类的研究方法也往往忽视垃圾分类问题的特点㊂对垃圾分类问题,尽管实际垃圾图片背景往往杂乱无规则,待识别的目标也形态变化多样,甚至处于多变的光照环境中,但图像中的语义概念和各个语义概念的相互联系却清晰且值得探索㊂例如,无论鸡蛋处于何种形态,其语义应该是一致的,并且鸡蛋等垃圾的出现往往伴随着厨房等背景环境㊂基于上述语义一致性与语义关联性的观察,笔者提出一种利用面向垃圾图像分类的残差语义强化网络㊂该方法的网络流程如图1所示,该网络主要包括4个部分:图像预处理㊁骨干网络㊁视觉概念推理和语义特征融合模块㊂图1 网络流程图Fig.1 Structure of networks 1.1 图像预处理为提高模型的泛化能力,首先需要进行数据样本扩充,从而保证采样数据的多样性以及全面性㊂笔者分别针对训练集和验证集设计了不同的数据增强操作㊂对训练集数据首先将图片进行随机大小裁剪,将其裁剪为大小为224×224像素的图片;再通过随机水平翻转进行数据增强;随后将图片数据转换为张量(Tensor)用于后续处理;最后对Tensor 进行归一化处理,使输入网络的图像像素值位于0~1区间㊂而对验证集数据,不同于训练集,其数据增强操作较为简单㊂首先将图片进行中心裁剪,将其裁剪为大小为224×224像素的图片;再将图片数据转换为Tensor 用于后续处理;最后对Tensor 进行归一化处理㊂对训练集和验证集的数据增强如图2所示㊂图2 数据集图像预处理流程Fig.2 Dataset image preprocessing 1.2 骨干网络选取图像分类任务要提高模型的准确率,需要选择加深或加宽深度神经网络,但随着参数量的增加(比如通道数,filter 大小等),网络设计的难度和计算开销会急剧增加,网络的实时性和轻量性会极大降低,甚至可能出现网络退化等问题㊂Xie 等[23]提出的ResNeXt 网络首先通过残差结构(见图3)有效地解决梯度弥散等问题㊂更重要的是他们采用分割⁃转换⁃合并思想,相较于ResNet 网络,ResNeXt 网络2301吉林大学学报(信息科学版)第41卷图3 ResNeXt 残差块结构Fig.3 Structure of residual block in ResNeXt 引入了基数概念,利用块分组思想,在不额外增加计算量的前提下提升效果,且效果优于提升深度和宽度㊂鉴于垃圾图像分类往往类别多样复杂且对特征提取能力要求高㊂此外,需要对特征组内的语义特征进行分析,并对不同特征组之间的语义关联进行建模㊂而ResNeXt 的基本思想恰好在本质上满足分层特征提取和特征语义分组设想㊂考虑到权衡模型的训练成本和性能,在本文中,笔者选择ResNeXt⁃50网络作为基础框架㊂1.3 特征语义关联强化视觉概念推理网络[24]在分割⁃转换⁃合并思想基础上,进一步提出了分割⁃转换⁃参与⁃交互⁃调制⁃合并的模块化多分支架构,能对高级视觉概念进行推理,有效地捕获全局背景,使模型性能得到有效提升㊂为强化分类特征中的目标语义并利用语义间相互关系增强分类特征的差异性,笔者对ResNeXt⁃50第4个阶段的32个特征分组采用视觉概念采样㊁推理和调制进行处理㊂3个模块组成一个模块化的多分支残差块以进行上述视觉概念的提取和推理㊂3个模块的具体结构和作用如下㊂1)视觉概念采样模块主要实现提取分组特征各自的视觉概念㊂将输入数据X ∈R HW ×d 通过1×1的分组卷积进行紧凑分组,这与ResNeXt 中参数分组的思想相类似,并表示为X ∈R HW ×p ,其中d =c ×p ,这里c 表示分组数,定义每组都为一个概念C ㊂其再通过一组3×3的卷积进行概念转化,得到概念特征映射Z c ㊂概念特征映射Z c 再通过聚合机制提取表示视觉概念的抽象特征向量,称其为视觉概念状态h c ㊂笔者采用基于注意力机制方式对输入特征进行提取㊂注意力机制通过将一个查询向量q c 和一组可互换的键值向量对K c 和V c 映射到一个向量,它能自适应地选择局部描述符集合㊂得到h c 如下:h c =softmax q c (Z c W k c )T æèçöø÷p′Z æèçöø÷c W v c ,(1)其中q c ∈R 1×p′,K c =Z c W k c ,V c =Z c W v c ,W k c 和W v c 是可学习权重㊂2)视觉概念推理模块实现模拟视觉概念之间的相互影响,实现概念状态之间的交互和更新㊂通过定义一个全连接图G =(V ,ε),其中V 是节点,ε是方向边,节点与前文得出的视觉概念状态h c 一一对应,通过定义边缘权重,即可对视觉概念状态h c 之间进行交互和更新㊂将更新后的视觉概念状态表示为h ′c,其更新规则如下:h ′c =ReLU BN h c +∑c c′=1A [c ,c′]h ()()c′,(2)其中通过一个邻接矩阵A ∈R C ×C 表示边缘权重值,可定义为A [c ,:]=tanh(h c W edge ),通过投影权重W edge 学习每个概念与其他概念之间的自适应关系,以此获取边缘权重㊂3)视觉概念调制模块实现对输出概念特征的调制,以解决信息传播时的维度不匹配问题㊂更新后的视觉概念状态h ′c除了拥有自己的概念信息,还获得了其他概念信息,这些信息必须进一步传播到从网络主流中提取的局部概念特征中㊂通过对通道进行缩放和平移进行概念特征映射的调制以解决维度不匹配问题㊂调制公式如下:X ′c =ReLU(αc X c +βc ),(3)其中αc 和βc 分别是缩放和移位的参数㊂为进行像素级调制,引入注意力图M c ∈R HW ×1定义缩放和位移参数,其中M c =softmax q c (Z c W k c )T æèçöø÷p′㊂其表达式是式(1)计算概念状态h c 的一部分,因此Kim 等[24]做出M c 包含了与概念相关空间信息的假设㊂基于此,可以将注意力图M c 进行归一化操作得到M ′c =M c max(M c ),用于将更新后的概念状态h ′c 投影到所有的局部位置㊂所以式(3)中αc 和3301第6期苏雯,等:面向垃圾图像分类的残差语义强化网络4301吉林大学学报(信息科学版)第41卷βc定义为αc=(M′c h′c)W scale c+b scale c, βc=(M′c h′c)W shift c+b shift c,(4)其中W scale c,W shift c分别是可学习的缩放和移动权重,b scale c,b shift c分别是缩放和移动偏置㊂笔者采用上述3个模块共同组成一个模块化的多分支残差块,将其并入骨干网络ResNeXt⁃50中,通过其对输入图像特征的高级视觉概念进行提取和强化得到语义强化特征㊂同时将ResNeXt⁃50网络低层块的输出经过注意力模块的调制,滤除在空间和通道上的冗余信息㊂再将其与提取出的视觉概念强化特征进行融合,从而提高ResNeXt网络的特征提取能力,增强模型性能㊂1.4 强化语义特征融合受残差思想的影响,笔者也认为每个附加层都应该更容易地包含原始函数作为其元素之一㊂并非通过堆叠不同的层找到可将输入特征映射为语义强化特征的函数,而是通过语义视觉概念的采样,交互更新以及调制学习输入特征到语义强化特征的不同㊂因此,特征语义关联强化模块作为输入特征的模块化多分支残差块,需要与输入特征的注意力调制分支融合㊂特征语义关联强化模块的输出X′c∈R HW×d与骨干网络第1阶段的输出F∈R H′W′×d′共同作为强化语义特征融合模块的输入,对F的注意力调制公式如下:F′=M c(F)⊗F,(5)F″=M s(F′)⊗F′,(6)其中M c∈R C×1×1是1D通道注意力特征,M s∈R1×H′×W′是2D空间注意力特征㊂经注意力机制处理后的特征将进一步与强化语义特征融合,形成直接用于预测分类的分类特征,融合公式如下:X Fusion=αF″+βX′c,(7)其中α和β是超参数,通过栅格验证方法,根据经验设置取值范围为(0,1),步长为0.2㊂特征语义关联强化模块所提取的语义特征虽然侧重于强化图像特征的高级视觉概念,但对底层特征中诸如空间轮廓定位及边缘信息的提取不完全,笔者通过注意力机制[14]从骨干网络的浅层提取这些底层特征,滤除在空间和通道上的冗余信息,其中空间注意力机制用于识别空间分辨率上的主要物体,通道注意力机制用于提取特征通道上的主要信息㊂通过空间注意力和通道注意力两种方式调整较浅层深度特征,减小其与强化语义特征融合时的信息代沟,消除强化语义特征融合时存在的语义层次及空间分辨率的差距,达到特征信息互补的目的㊂2 实验与分析2.1 数据集为验证笔者所提出方法的有效性,在广泛使用的开源数据集上进行了定性及定量的实验验证㊂选取了在Kaggle中的垃圾分类公开数据集以及开源的TransNet数据集㊂其中Kaggle垃圾数据集包含来自12个不同类别的生活垃圾图像,共计15515张㊂类别分为纸张㊁硬纸板㊁生物㊁金属㊁塑料㊁棕色玻璃㊁绿色玻璃㊁白色玻璃㊁衣服㊁鞋子㊁电池和废弃物㊂笔者参考王玉等[6]的处理方式,只选其中纸张㊁硬纸板㊁金属㊁塑料㊁白色玻璃和废弃物6个类别进行对比实验,将数据集增广至2倍后的70%的图片用于训练,30%的图片用于测试㊂对消融实验,笔者则选用Kaggle垃圾数据集的全部12分类进行实验,训练集㊁验证集㊁测试集划分为7∶2∶1㊂TransNet数据集一共包含硬纸板㊁玻璃㊁金属㊁纸张㊁塑料和废弃物垃圾6个类别,共2527张图像,数据集划分参考Aral等[25]在其基于深度模型的TrashNet数据集分类中的比例,将训练集㊁测试集和验证集划分为7∶1.7∶1.3㊂2.2 厨余垃圾数据集笔者采集并标定的厨余垃圾数据集(RKG Dataset)由工业摄像头采集,涵盖真实厨房㊁餐桌㊁水槽㊁垃圾桶等各种场景,图片包括菜梗叶㊁茶叶渣㊁蛋壳㊁骨头㊁烘焙糕点㊁剩饭菜㊁水果等七大类垃圾共13227幅各类分辨率图像,光照条件覆盖了生活中可能出现的各种情况,例如自然光照及室内光照等㊂部分采集RKG Dataset 数据集图像实例如图4,图5所示㊂推荐的训练测试比例为:训练集11097张图片,平均每个类别1701张;测试集1321张图片,平均每个类别188张㊂目前该数据集已经开源:https:∥ /ugawahm /Residual⁃Semantic⁃Reinforcement⁃Network⁃for⁃Garbage⁃Classification㊂笔者所提方法在该数据集的准确率为84.17%㊂图4 部分数据集图像实例Fig.4 Examples of partial dataset images 图5 RKG Dataset 示例图片Fig.5 Sample images of the RKG Dataset 2.3 实现细节实验设备操作系统为Ubuntu 20.04,显卡为英伟达RTX 3090Ti,显存24GByte,实验使用Pytorch 框架㊂图像预处理阶段,笔者统一将输入的图像裁剪为224×224像素大小,并将归一化参数设置为[0.485,0.456,0.406],[0.229,0.224,0.225]㊂网络训练选用交叉损失熵函数作为损失函数,优化器选用Adam 优化器,以自适应调整训练过程中的学习率,其中学习率设置为0.0001,Weight decay 设置为0,betas 设置为[0.99,0.999]㊂2.4 RKG Dataset 测试实验笔者方法与不同网络在RKG Dataset 分类数据集下的对比列入表1㊂实验结果表明,笔者算法在RKG Dataset 数据集上也具有相对较好的效果和泛化能力㊂5301第6期苏雯,等:面向垃圾图像分类的残差语义强化网络表1 RKG 测试实验(ResNeXt⁃50+Semantic Aggregation 简写为ResNeXt⁃50+SA )笔者将输入图像经过骨干网络ResNeXt⁃50后得到的特征尺寸为7×7,通道数为2048的特征向量作为用于概念提取的多分支残差块的输入㊂最后将输出结果进行自适应平均池化操作,再经过全连接层输出㊂对Kaggle 中的垃圾分类公开数据集,笔者将数据的批处理大小设置为32,训练迭代200次,得出在6分类垃圾数据集的预测平均准确率为95.3%㊂其中,纸张㊁硬纸板㊁金属㊁塑料㊁废弃物和白色玻璃的预测率分别为97.3%㊁94.3%㊁94.1%㊁95.0%㊁97.1%和93.8%㊂其混淆矩阵如图6所示,同时各类别召回率㊁精确率如表2所示,其平均准确率为95.3%,平均召回率为95.2%,平均精确率为99.1%㊂通过分析发现预测准确率较低的类别,如白色玻璃,其图像特征大都为透明或者白色为主,对这类具有透视性质的目标,目前的语义提取神经网络难以捕获其中有利于分类的信息㊂表2 笔者方法在Kaggle 6分类垃圾数据集上的准确率,召回率和精确率指标图6 Kaggle 6分类垃圾数据集测试集混淆矩阵Fig.6 Confusion matrix for the test set of Kaggle 6⁃classification garbage dataset 数据集提出的DRSN [6]的结果对比如表3所示㊂从表3可看出,笔者提出的方法较DRSN 网络在性能上有较大提升,这得益于视觉概念推理网络对特征视觉概念的提取能力㊂同时笔者的方法也与Aral 等[25]和Fatma 等[27]在公开数据集TrashNet 上的各种方法进行对比㊂对比结果如表4所示㊂由表4可见,笔者提出的方法最终预测准确率优于MobileNet [27],Inception ResNet V2[28],DenseNet121[29]以及Fatma 等[27]提出的VT⁃MLH⁃CNN 网络㊂表3 Kaggle 6分类垃圾数据集对比表4 TrashNet 数据集对比6301吉林大学学报(信息科学版)第41卷2.6 消融实验笔者提出方法与不同网络在Kaggle 12分类数据集下的对比如表5所示㊂表5 消融实验(ResNeXt⁃50+Semantic Aggregation 简写为ResNeXt⁃50+SA )图7 Kaggle 12分类垃圾数据集测试集混淆矩阵Fig.7 Confusion matrix for the test set of Kaggle 12⁃classification garbage dataset 提出的ResNeXt 网络在ResNet 的基础上更新了块结构,在提高预测准确率的前提下减少了计算量㊂而笔者提出的方法通过对视觉概念的提取和推理更好地抓取特征,能保持在训练速度几乎不变的情况下较骨干网络ResNeXt⁃50的准确率提升了1.23%㊂笔者方法在测试集上的混淆矩阵如图7所示㊂实验在ResNeXt⁃50(baseline )上的准确率高于VGG⁃16和ResNet⁃50,可见笔者的基准网络选择合适,而且ResNeXt 的基本思想恰好在本质上满足笔者分层特征提取和特征语义分组设想㊂ResNeXt⁃50+SA 较ResNeXt⁃50(baseline)提升了0.35%,说明特征语义关联强化模块可以对高级视觉概念进行推理,有效地捕获全局背景,使模型性能得到有效提升㊂而笔者的方法较ResNeXt⁃50(baseline),ResNeXt⁃50+SA 以及ResNeXt⁃50+CBAM 分别提升了1.23%,0.88%和1.12%㊂说明虽然注意力机制的加入能提高模型分类的准确率,但简单的注意力运算并不能提取分组深度特征中的语义概念,也无法很好地对语义概念之间的相互影响进行建模更新㊂同样,虽然特征语义关联强化模块的加入,使深度特征中的语义概念得以加强,但同时损失了对垃圾分类有益的深度特征中低层次特征㊂笔者提出的网络消除强化语义特征融合时存在的语义层次及空间分辨率的差距,达到特征信息互补的目的㊂为获得分类精确度最优的特征融合方式,笔者尝试使用了3种特征融合方式,分别为两个特征相加,两个特征加权融合(F″,X ′c权重为[0.4,0.6])以及特征直接相连接㊂实验结果如表6,表7所示㊂结果表明基于特征加权融合且权重为[0.4,0.6]时融合效果最佳㊂这也与笔者的动机相吻合㊂经过注意力机制调整的特征与强化语义特征在特征表征的侧重点有所不同,因此依赖权重融合的方法得到了更好的效果㊂表6 不同融合方式对比表7 不同融合权重对比7301第6期苏雯,等:面向垃圾图像分类的残差语义强化网络8301吉林大学学报(信息科学版)第41卷2.7 参数对比实验将笔者算法与ResNeXt⁃50以及ResNeXt⁃101进行对比,结果如表8所示㊂可见,笔者提出的算法相比参数量巨大的深层网络ResNeXt⁃101,能在ResNeXt⁃50的基础上增加较少参数,从而提升了准确率㊂表8 参数数量对比3 结 语笔者提出了一种面向垃圾图像分类的残差语义强化网络,由图像预处理㊁骨干网络㊁视觉概念推理㊁语义特征融合模块构成㊂图像预处理模块对训练集以及测试集的图片进行随机裁剪㊁翻转等操作,大大提高了模型的泛化能力;骨干网络模块利用块分组思想,在不额外增加计算量前提下提升效果,且效果优于提升深度和宽度;视觉概念推理模块使用了分割⁃转换⁃参与⁃交互⁃调制⁃合并的模块化多分支架构,能对高级视觉概念进行推理,有效地捕获全局背景,使模型性能得到有效提升;语义特征融合模块通过空间注意力和通道注意力两种方式调整较浅层深度特征,减小其与强化语义特征融合时的信息代沟,消除强化语义特征融合时存在的语义层次及空间分辨率的差距,达到特征信息互补的目的㊂笔者提出的方法在Kaggle12分类垃圾数据集上进行了实验,预测准确率达92.49%;并在RKG Dataset数据集上进行了实验,预测准确率达84.17%,可见该算法能较好地满足实际应用需求㊂今后还将考虑提升网络深度,更改骨干网络模型,以进一步提高预测准确率㊂参考文献:[1]KRIZHEVSKY A,SUTSKEVER I,HINTON G.Imagenet Classification with Deep Convolutional Neural Networks[C]∥Proceedings of the25th International Conference on Neural Information Processing Systems⁃Volume1.Red Hook,NY,USA: [s.n.],2012:1097⁃1105.[2]HE K M,ZHANG X Y,REN S Q,et al.Deep Residual Learning for Image Recognition[C]∥Proceedings of the2016IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Las Vegas,NV,USA:IEEE,2016:770⁃778. [3]李妍.基于ResNet算法的垃圾图像识别分类研究[J].长江信息通信,2021,34(5):25⁃27.LI Y.Research on Garbage Image Recognition and Classification Based on ResNet Algorithm[J].Changjiang Information& Communications,2021,34(5):25⁃27.[4]王超,万兆江,周瑜杰,等.基于ResNet⁃50垃圾分类算法的改进及应用[J].智能计算机与应用,2022,12(10): 184⁃188.WANG C,WAN Z J,ZHOU Y J,et al.Research on Garbage Classification Algorithm Based on Improved ResNet⁃50[J]. Intelligent Computer and Applications,2022,12(10):184⁃188.[5]张丽艳,赵艺璇,牟钰鹏,等.基于ResNet50的列车常见垃圾自动分类算法[J].大连交通大学学报,2021,42(4): 101⁃105.ZHANG L Y,ZHAO Y X,MOU Y P,et al.Automatic Sorting Method for Common Garbage in Train Based on ResNet50[J]. Journal of Dalian Jiaotong University,2021,42(4):101⁃105.[6]王玉,张燕红,周昱洲,等.基于深度残差收缩网络的校园垃圾图像分类[J].吉林大学学报(信息科学版),2023, 41(1):186⁃192.WANG Y,ZHANG Y H,ZHOU Y Z,et al.Garbage Image Classification of Campus Based on Deep Residual Shrinkage Network[J].Journal of Jilin University(Information Science Edition),2023,41(1):186⁃192.[7]徐明明,高丙朋,黄家興.改进残差网络的轻量级塑料垃圾分类研究[J].现代电子技术,2022,45(17):95⁃99. XU M M,GAO B P,HUANG J J.Research on Lightweight Plastic Waste Classification by Improving Residual Networks[J]. Modern Electronic Technology,2022,45(17):95⁃99.[8]李晓旭,刘忠源,武继杰,等.小样本图像分类的注意力全关系网络[J].计算机学报,2023,46(2):371⁃384.。
融合知识图谱与注意力机制的短文本分类模型
第47卷第1期Vol.47No.1计算机工程Computer Engineering2021年1月January2021融合知识图谱与注意力机制的短文本分类模型丁辰晖1，夏鸿斌1，2，刘渊1，2（1.江南大学数字媒体学院，江苏无锡214122；2.江苏省媒体设计与软件技术重点实验室，江苏无锡214122）摘要：针对短文本缺乏上下文信息导致的语义模糊问题,构建一种融合知识图谱和注意力机制的神经网络模型。
借助现有知识库获取短文本相关的概念集,以获得短文本相关先验知识,弥补短文本缺乏上下文信息的不足。
将字符向量、词向量以及短文本的概念集作为模型的输入,运用编码器-解码器模型对短文本与概念集进行编码,利用注意力机制计算每个概念权重值,减小无关噪声概念对短文本分类的影响,在此基础上通过双向门控循环单元编码短文本输入序列,获取短文本分类特征,从而更准确地进行短文本分类。
实验结果表明,该模型在AGNews、Ohsumed 和TagMyNews短文本数据集上的准确率分别达到73.95%、40.69%和63.10%,具有较好的分类能力。
关键词：短文本分类；知识图谱；自然语言处理；注意力机制；双向门控循环单元开放科学（资源服务）标志码（OSID）：中文引用格式：丁辰晖，夏鸿斌，刘渊.融合知识图谱与注意力机制的短文本分类模型［J］.计算机工程，2021，47（1）：94-100.英文引用格式：DING Chenhui，XIA Hongbin，LIU Yuan.Short text classification model combining knowledge graph and attention mechanism［J］.Computer Engineering，2021，47（1）：94-100.Short Text Classification Model Combining Knowledge Graph and Attention MechanismDING Chenhui1，XIA Hongbin1，2，LIU Yuan1，2（1.School of Digital Media，Jiangnan University，Wuxi，Jiangsu214122，China；2.Jiangsu Key Laboratory of Media Design andSoftware Technology，Wuxi，Jiangsu214122，China）【Abstract】Concerning the semantic ambiguity caused by the lack of context information，this paper proposes a neural network model，which combines knowledge graph and attention mechanism.By using the existing knowledge base to obtain the concept set related to the short text，the prior knowledge related to the short text is obtained to address the lack of context information in the short text.The character vector，word vector，and concept set of the short text are taken as the input of the model.Then the encoder-decoder model is used to encode the short text and concept set，and the attention mechanism is used to calculate the weight value of each concept to reduce the influence of unrelated noise concepts on short text classification.On this basis，a Bi-directional-Gated Recurrent Unit（Bi-GRU）is used to encode the input sequences of the short text to obtain short text classification features，so as to perform short text classification more effectively.Experimental results show that the accuracy of the model on AGNews，Ohsumed and TagMyNews short text data sets is73.95%，40.69%and63.10%，respectively，showing a good classification ability.【Key words】short text classification；knowledge graph；Natural Language Processing（NLP）；attention mechanism；Bi-directional-Gated Recurrent Unit（Bi-GRU）DOI：10.19678/j.issn.1000-3428.00567340概述近年来，随着Twitter、微博等社交网络的出现，人们可以轻松便捷地在社交平台上发布文本、图片、视频等多样化的信息，社交网络已超越传统媒体成为新的信息聚集地，并以极快的速度影响着社会的信息传播格局［1］。
Senserelations语义关系
Semantic relationships are the foundation of language understanding. By analyzing semantic relationships, one can understand the meaning of words and sentences, and thus comprehend the meaning of the entire text.
要点一
要点二
Detailed description
Semantic conflict refers to the situation where two concepts or entities are contradictory or mutually exclusive in meaning and nature. For example, "peace" and "war" are conflicting because they represent opposite meanings and states.
Semantic relevance
Refers to the existence or attribute of one concept or entity containing the existence or attribute of another concept or entity.
Summary word
Statistical methods
Deep learning based methods
Summary: Based on deep learning methods, neural network models are used to recognize and calculate semantic relationships by learning semantic patterns from corpora.
classification
classificationClassification is a fundamental task in machine learning and data analysis. It involves categorizing data into predefined classes or categories based on their features or characteristics. The goal of classification is to build a model that can accurately predict the class of new, unseen instances.In this document, we will explore the concept of classification, different types of classification algorithms, and their applications in various domains. We will also discuss the process of building and evaluating a classification model.I. Introduction to ClassificationA. Definition and Importance of ClassificationClassification is the process of assigning predefined labels or classes to instances based on their relevant features. It plays a vital role in numerous fields, including finance, healthcare, marketing, and customer service. By classifying data, organizations can make informed decisions, automate processes, and enhance efficiency.B. Types of Classification Problems1. Binary Classification: In binary classification, instances are classified into one of two classes. For example, spam detection, fraud detection, and sentiment analysis are binary classification problems.2. Multi-class Classification: In multi-class classification, instances are classified into more than two classes. Examples of multi-class classification problems include document categorization, image recognition, and disease diagnosis.II. Classification AlgorithmsA. Decision TreesDecision trees are widely used for classification tasks. They provide a clear and interpretable way to classify instances by creating a tree-like model. Decision trees use a set of rules based on features to make decisions, leading down different branches until a leaf node (class label) is reached. Some popular decision tree algorithms include C4.5, CART, and Random Forest.B. Naive BayesNaive Bayes is a probabilistic classification algorithm based on Bayes' theorem. It assumes that the features are statistically independent of each other, despite the simplifying assumption, which often doesn't hold in the realworld. Naive Bayes is known for its simplicity and efficiency and works well in text classification and spam filtering.C. Support Vector MachinesSupport Vector Machines (SVMs) are powerful classification algorithms that find the optimal hyperplane in high-dimensional space to separate instances into different classes. SVMs are good at dealing with linear and non-linear classification problems. They have applications in image recognition, hand-written digit recognition, and text categorization.D. K-Nearest Neighbors (KNN)K-Nearest Neighbors is a simple yet effective classification algorithm. It classifies an instance based on its k nearest neighbors in the training set. KNN is a non-parametric algorithm, meaning it does not assume any specific distribution of the data. It has applications in recommendation systems and pattern recognition.E. Artificial Neural Networks (ANN)Artificial Neural Networks are inspired by the biological structure of the human brain. They consist of interconnected nodes (neurons) organized in layers. ANN algorithms, such asMultilayer Perceptron and Convolutional Neural Networks, have achieved remarkable success in various classification tasks, including image recognition, speech recognition, and natural language processing.III. Building a Classification ModelA. Data PreprocessingBefore implementing a classification algorithm, data preprocessing is necessary. This step involves cleaning the data, handling missing values, and encoding categorical variables. It may also include feature scaling and dimensionality reduction techniques like Principal Component Analysis (PCA).B. Training and TestingTo build a classification model, a labeled dataset is divided into a training set and a testing set. The training set is used to fit the model on the data, while the testing set is used to evaluate the performance of the model. Cross-validation techniques like k-fold cross-validation can be used to obtain more accurate estimates of the model's performance.C. Evaluation MetricsSeveral metrics can be used to evaluate the performance of a classification model. Accuracy, precision, recall, and F1-score are commonly used metrics. Additionally, ROC curves and AUC (Area Under Curve) can assess the model's performance across different probability thresholds.IV. Applications of ClassificationA. Spam DetectionClassification algorithms can be used to detect spam emails accurately. By training a model on a dataset of labeled spam and non-spam emails, it can learn to classify incoming emails as either spam or legitimate.B. Fraud DetectionClassification algorithms are essential in fraud detection systems. By analyzing features such as account activity, transaction patterns, and user behavior, a model can identify potentially fraudulent transactions or activities.C. Disease DiagnosisClassification algorithms can assist in disease diagnosis by analyzing patient data, including symptoms, medical history, and test results. By comparing the patient's data againsthistorical data, the model can predict the likelihood of a specific disease.D. Image RecognitionClassification algorithms, particularly deep learning algorithms like Convolutional Neural Networks (CNNs), have revolutionized image recognition tasks. They can accurately identify objects or scenes in images, enabling applications like facial recognition and autonomous driving.V. ConclusionClassification is a vital task in machine learning and data analysis. It enables us to categorize instances into different classes based on their features. By understanding different classification algorithms and their applications, organizations can make better decisions, automate processes, and gain valuable insights from their data.。
基于知识图谱使用多特征语义融合的文档对匹配
第 54 卷第 8 期2023 年 8 月中南大学学报(自然科学版)Journal of Central South University (Science and Technology)V ol.54 No.8Aug. 2023基于知识图谱使用多特征语义融合的文档对匹配陈毅波1，张祖平2，黄鑫1，向行1，何智强1(1. 国网湖南省电力有限公司，湖南 长沙，410004；2. 中南大学 计算机学院，湖南 长沙，410083)摘要：为了区分文档间的同源性和异质性，首先，提出一种多特征语义融合模型(Multi-Feature Semantic Fusion Model ，MFSFM)来捕获文档关键字，它采用语义增强的多特征表示法来表示实体，并在多卷积混合残差CNN 模块中引入局部注意力机制以提高实体边界信息的敏感性；然后，通过对文档构建一个关键字共现图，并应用社区检测算法检测概念进而表示文档，从而匹配文档对；最后，建立两个多特征文档数据集，以验证所提出的基于MFSFM 的匹配方法的可行性，每一个数据集都包含约500份真实的科技项目可行性报告。
研究结果表明：本文所提出的模型在CNSR 和CNSI 数据集上的分类精度分别提高了13.67%和15.83%，同时可以实现快速收敛。
关键词：文档对匹配；多特征语义融合；知识图谱；概念图中图分类号：TP391 文献标志码：A 文章编号：1672-7207（2023）08-3122-10Matching document pairs using multi-feature semantic fusionbased on knowledge graphCHEN Yibo 1, ZHANG Zuping 2, HUANG Xin 1, XIANG Xing 1, HE Zhiqiang 1(1. State Grid Hunan Electric Power Company Limited, Changsha 410004, China;2. School of Computer Science and Engineering, Central South University, Changsha 410083, China)Abstract: To distinguish the homogeneity and heterogeneity among documents, a Multi-Feature Semantic Fusion Model(MFSFM) was firstly proposed to capture document keywords, which employed a semantically enhanced multi-feature representation to depict entities. A local attention mechanism in the multi-convolutional mixed residual CNN module was introduced to enhance sensitivity to entity boundary information. Secondly, a keyword co-occurrence graph for documents was constructed and a community detection algorithm was applied to represent收稿日期： 2022 −05 −15； 修回日期： 2022 −09 −09基金项目(Foundation item)：湖南省电力物联网重点实验室项目(2019TP1016)；电力知识图谱关键技术研究项目(5216A6200037)；国家自然科学基金资助项目(72061147004)；湖南省自然科学基金资助项目( 2021JJ30055) (Project (2019TP1016) supported by Hunan Key Laboratory for Internet of Things in Electricity; Project(5216A6200037) supported by key Technologies of Power Knowledge Graph; Project(72061147004) supported by the National Natural Science Foundation of China; Project(2021JJ30055) supported by the Natural Science Foundation of Hunan Province)通信作者：张祖平，博士，教授，从事大数据分析与处理研究；E-mail ：***************.cnDOI: 10.11817/j.issn.1672-7207.2023.08.016引用格式： 陈毅波, 张祖平, 黄鑫, 等. 基于知识图谱使用多特征语义融合的文档对匹配[J]. 中南大学学报(自然科学版), 2023, 54(8): 3122−3131.Citation: CHEN Yibo, ZHANG Zuping, HUANG Xin, et al. Matching document pairs using multi-feature semantic fusion based on knowledge graph[J]. Journal of Central South University(Science and Technology), 2023, 54(8): 3122−3131.第 8 期陈毅波，等：基于知识图谱使用多特征语义融合的文档对匹配concepts, thus facilitating document was matching. Finally, two multi-feature document datasets were established to validate the feasibility of the proposed MFSFM-based matching approach, with each dataset comprising approximately 500 real feasibility reports of scientific and technological projects. The results indicate that the proposed model achieves an increase in classification accuracy of 13.67% and 15.83% on the CNSR and CNSI datasets, respectively, and demonstrates rapid convergence.Key words: document pairs matching; multi-feature semantic fusion; knowledge graph; concept graph识别文档对的关系是一项自然语言理解任务，也是文档查重和文档搜索工作必不可少的步骤。
数据分析英语试题及答案
数据分析英语试题及答案一、选择题（每题2分，共10分）1. Which of the following is not a common data type in data analysis?A. NumericalB. CategoricalC. TextualD. Binary2. What is the process of transforming raw data into an understandable format called?A. Data cleaningB. Data transformationC. Data miningD. Data visualization3. In data analysis, what does the term "variance" refer to?A. The average of the data pointsB. The spread of the data points around the meanC. The sum of the data pointsD. The highest value in the data set4. Which statistical measure is used to determine the central tendency of a data set?A. ModeB. MedianC. MeanD. All of the above5. What is the purpose of using a correlation coefficient in data analysis?A. To measure the strength and direction of a linear relationship between two variablesB. To calculate the mean of the data pointsC. To identify outliers in the data setD. To predict future data points二、填空题（每题2分，共10分）6. The process of identifying and correcting (or removing) errors and inconsistencies in data is known as ________.7. A type of data that can be ordered or ranked is called________ data.8. The ________ is a statistical measure that shows the average of a data set.9. A ________ is a graphical representation of data that uses bars to show comparisons among categories.10. When two variables move in opposite directions, the correlation between them is ________.三、简答题（每题5分，共20分）11. Explain the difference between descriptive andinferential statistics.12. What is the significance of a p-value in hypothesis testing?13. Describe the concept of data normalization and its importance in data analysis.14. How can data visualization help in understanding complex data sets?四、计算题（每题10分，共20分）15. Given a data set with the following values: 10, 12, 15, 18, 20, calculate the mean and standard deviation.16. If a data analyst wants to compare the performance of two different marketing campaigns, what type of statistical test might they use and why?五、案例分析题（每题15分，共30分）17. A company wants to analyze the sales data of its products over the last year. What steps should the data analyst take to prepare the data for analysis?18. Discuss the ethical considerations a data analyst should keep in mind when handling sensitive customer data.答案：一、选择题1. D2. B3. B4. D5. A二、填空题6. Data cleaning7. Ordinal8. Mean9. Bar chart10. Negative三、简答题11. Descriptive statistics summarize and describe thefeatures of a data set, while inferential statistics make predictions or inferences about a population based on a sample.12. A p-value indicates the probability of observing the data, or something more extreme, if the null hypothesis is true. A small p-value suggests that the observed data is unlikely under the null hypothesis, leading to its rejection.13. Data normalization is the process of scaling data to a common scale. It is important because it allows formeaningful comparisons between variables and can improve the performance of certain algorithms.14. Data visualization can help in understanding complex data sets by providing a visual representation of the data, making it easier to identify patterns, trends, and outliers.四、计算题15. Mean = (10 + 12 + 15 + 18 + 20) / 5 = 14, Standard Deviation = √[(Σ(xi - mean)^2) / N] = √[(10 + 4 + 1 + 16 + 36) / 5] = √52 / 5 ≈ 3.816. A t-test or ANOVA might be used to compare the means ofthe two campaigns, as these tests can determine if there is a statistically significant difference between the groups.五、案例分析题17. The data analyst should first clean the data by removing any errors or inconsistencies. Then, they should transformthe data into a suitable format for analysis, such ascreating a time series for monthly sales. They might also normalize the data if necessary and perform exploratory data analysis to identify any patterns or trends.18. A data analyst should ensure the confidentiality andprivacy of customer data, comply with relevant data protection laws, and obtain consent where required. They should also be transparent about how the data will be used and take steps to prevent any potential misuse of the data.。
计算机专业英语(张强华-第二版)重点单词及部分课后答案
Unit 1单词：〖Ex. 3〗根据下面的英文解释，写出相应的英文词汇（使用学过的单词、词组或缩略语）〖Ex. 5〗把下列短文翻译成中文系统面板和普通键盘有专门控制键，你可以使用这些控制键实现主要的多媒体功能：观相片、听音乐和看电影。
面板还有为看电视和阅读电视指南而设的快速启动按钮。
Ex. 9〗用that从句做宾语将下面汉语译成英语你应该知道，你不仅仅能读取磁盘上的数据，也能够往上面写新的信息1. You should know that you can not only read data from the disk but also write new information to it.你应该意识到，软盘容纳不了多少数据2. You should realize that floppies do not hold too much data.我们计算机老实说，USB要比火线慢多了3. Our computer teacher said that USB is much slower than Firewire.我认为CPU主要责任是执行指令4. I think/believe that the CPU is primarily responsible for executing instructions.Unit 2单词〖Ex. 3〗根据下面的英文解释，写出相应的英文词汇（使用学过的单词、词组或缩略语）Unit 3〖Ex. 3〗根据下面的英文解释，写出相应的英文词汇（使用学过的单词、词组或缩略语）Unit 4单词〖Ex. 3〗根据下面的英文解释，写出相应的英文词汇（使用学过的单词、词组或缩略语）Unit 5单词〖Ex. 3〗根据下面的英文解释，写出相应的英文词汇（使用学过的单词、词组或缩略语）〖Ex. 5〗把下列短文翻译成中文佳能打印机有五种样式，价格从$80 到$500不等，满足了任何想打印相片用户的需求。
classification英语作文
classification英语作文Classification is an essential concept in various fields, including biology, psychology, linguistics, and computer science. It refers to the process of categorizing or grouping objects, ideas, or information based on their shared characteristics or attributes. This article aims to provide a comprehensive understanding of classification, its importance, and its applications across different domains.In simple terms, classification involves organizing items into groups or categories based on their similarities or differences. This process helps in organizing and understanding complex information, making it easier to analyze and interpret. Classification is often based on certain criteria or features that define the groups. These criteria can be quantitative, such as size or weight, or qualitative, such as color or shape.The importance of classification cannot be overstated. It allows us to make sense of the world around us by organizing and categorizing information. For example, in biology, organisms are classified into different taxonomic ranks based on their evolutionary relationships. This classification system helps scientists study and understand the diversity of life on Earth. In psychology, classification is used to diagnose and treat mental disorders by categorizing symptoms and behaviors. Similarly, in linguistics, words are classified into different parts of speech, facilitating language analysis and understanding.In computer science, classification plays a crucial role in machine learning and data analysis. Machine learning algorithms use classification techniques to automatically categorize data based on patterns and features. This enables computers to perform tasks such as spam filtering, sentiment analysis, and image recognition. Classification algorithms, such as decision trees, support vector machines, and neural networks, are widely used in various applications, including healthcare, finance, and marketing.The process of classification typically involves several steps. Firstly, the objects or data are collected and organized. Then, relevant features or attributes are identified. These features serve as the basis for creating categories or classes. Next, a classificationalgorithm is applied to assign each object to a particular class based on its features. The algorithm learns from existing data and uses this knowledge to classify new, unseen data. Finally, the accuracy of the classification is evaluated to assess the effectiveness of the algorithm.Classification has numerous practical applications. In healthcare, it is used for disease diagnosis and prognosis prediction. By classifying patient data, doctors can make informed decisions and provide personalized treatment plans. In finance, classification is employed for credit scoring, fraud detection, and stock market analysis. It helps identify patterns and anomalies in financial data, enabling timely interventions. In marketing, classification is used for customer segmentation and targeting. By classifying customers based on their preferences and behaviors, businesses can tailor their marketing strategies to specific groups.In conclusion, classification is a fundamental concept in various disciplines and plays a crucial role in organizing, analyzing, and understanding information. It allows us to make sense of complex data and facilitates decision-making processes. From biology to computer science, classification has diverse applications that contribute to advancements in different fields. With the increasing availability of data and advancements in machine learning, the importance of classification is only expected to grow in the future.。
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Abstract. Data classification is a process which groups objects with common properties into classes and produces a classification scheme over a set of data objects. Data classification is useful for understanding and organizing database data and building hierarchical schemes in databases. We investigate data classification in relational databases and develop a method for data classification by concept-based generalization. Our method applies an attribute-oriented generalization technique which utilizes the knowledge about data concepts, integrates a data classification process with relational operations, and provides an efficient way for classification of data in relational databases. The characteristics of each class can be extracted automatically in the classification process. Moreover, quantitative information can be registered in the generalization process to assist the classification of data based on database statistics. Our analysis of the classification algorithms shows that the attribute-oriented approach substantially reduces the complexity of data classification in large databases. Keywords : data classification, conceptual clustering, knowledge discovery in databases, attribute-oriented approach . 1. Introduction Data classification, a process which groups objects with common properties into classes and produces a classification scheme over a set of data objects, has been studied in cluster analysis, numerical taxonomy and machine learning research [1, 4, 6, 7, 11, 12, 14]. Data classification is also called data clustering [6, 14], however, the term data clustering has been used in traditional database literature [19] with a different meaning. Data clustering in the database literature refers to the technique which groups data with similar search keys into physically adjacent blocks for efficient access; while that used in concept classification refers to a technique which groups data according to some concept similarity measurement for semantic analysis. In order to construct meaningful classification of data objects, one needs to define a measure of similarity between the objects and then use it to determine classes. Classes are defined as collections of objects whose intraclass similarity is high and interclass similarity is low. Data classification is fundamental to research in many fields in social and natural sciences. Since databases potentially store a large amount of data, it is important to develop efficient methods for data classification in databases. Although data in a relational database are usually well-formatted and modeled by semantic data models [10], the contents of the data may not be classified. For example, a chemistry database may store a large amount of experimental data in a relational format, but knowledge and efforts are needed to classify the data in order to determine the intrinsic regularity of the data. Clearly, schemas and data formats are not equivalent to conceptual classes. The classification of database data will help us discover data characteristics, summarize data in an understandable manner, and organize data according to
† The work was supported in part by the Natural Sciences and Engineering Research Council of Canada under the operating grants A-3723/A-4309 and a research grant from Centre for Systems Science of Simon Fraser University.
Concept-Based Data Classification in Relational Databases †
Jiawei Han, Yandong Cai and Nick Cercone School of Computing Science Simon Fraser University Burnaby, British Columbia, Canada {han, cai, nick}@cs.sfu.ca
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knowledge-oriented structures. Many traditional data classification methods are based on some numerical taxonomy [1, 18], where clusters are determined solely on the basis of a predefined numerical measure of similarity. To define such a measure, a data analyst determines attributes that are perceived as relevant for characterizing objects under consideration. Vectors of values of these attributes for individual objects serve as descriptions of these objects. Considering attributes as dimensions of a multidimensional description space, each object description corresponds to a point in the space. The similarity between objects can thus be measured as a reciprocal function of the distance between the points in the description space. Such an approach has produced a number of efficient clustering algorithms [1]. However, the approach make