Data Classification: Algorithms and Applications最新文献_第2页

Text Classification 文本分类

Data Classification: Algorithms and Applications Pub Date : 1900-01-01 DOI: 10.1201/b17320-12

Nikolaos Mylonas, Ioannis Mollas, Grigorios Tsoumakas

引用次数: 2

Rare Class Learning 罕见的课堂学习

Data Classification: Algorithms and Applications Pub Date : 1900-01-01 DOI: 10.1201/b17320-18

C. Aggarwal

引用次数: 2

Support Vector Machines 支持向量机

Data Classification: Algorithms and Applications Pub Date : 1900-01-01 DOI: 10.1201/b17320-8

Po-Wei Wang, Chih-Jen Lin

{"title":"Support Vector Machines","authors":"Po-Wei Wang, Chih-Jen Lin","doi":"10.1201/b17320-8","DOIUrl":"https://doi.org/10.1201/b17320-8","url":null,"abstract":"The original SVM algorithm was invented by Vladimir N. Vapnik and the current standard incarnation (soft margin) was proposed by Corinna Cortes and Vapnik in 1993 and published in 1995. A support vector machine(SVM) constructs a hyperplane or set of hyperplanes in a highor infinitedimensional space, which can be used for classification, regression, or other tasks. Intuitively, a good separation is achieved by the hyperplane that has the largest distance to the nearest training data point of any class (so-called functional margin), since in general the larger the margin the lower the generalization error of the classifier. In this notes, we will explain the intuition and then get the primal problem, and how to translate the primal problem to dual problem. We will apply kernel trick and SMO algorithms to solve the dual problem and get the hyperplane we want to separate the dataset. Give general idea about SVM and introduce the goal of this notes, what kind of problems and knowledge will be covered by this node. In this note, one single SVM model is for two labels classification, whose label is y ∈ {−1, 1}. And the hyperplane we want to find to separate the two classes dataset is h, for which classifier, we use parameters w, b and we write our classifier as hw,b(x) = g(w x+ b) Here, g(z) = 1 if z ≥ 0, and g(z) = −1 otherwise.","PeriodicalId":378937,"journal":{"name":"Data Classification: Algorithms and Applications","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115417767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Visual Classification 视觉分类

Data Classification: Algorithms and Applications Pub Date : 1900-01-01 DOI: 10.1201/b17320-24

Giorgio Maria Di Nunzio

引用次数: 0

An Introduction to Data Classification 数据分类导论

Data Classification: Algorithms and Applications Pub Date : 1900-01-01 DOI: 10.1201/b17320-2

C. Aggarwal

引用次数: 17

Multimedia Classification 多媒体分类

Data Classification: Algorithms and Applications Pub Date : 1900-01-01 DOI: 10.1201/b17320-13

Shiyu Chang, Wei Han, Xianming Liu, N. Xu, Pooya Khorrami, Thomas S. Huang

引用次数: 2

Ensemble Learning 整体学习

Data Classification: Algorithms and Applications Pub Date : 1900-01-01 DOI: 10.1201/b17320-20

Yaliang Li, Jing Gao, Qi Li, Wei Fan

引用次数: 0

Transfer Learning 转移学习

Data Classification: Algorithms and Applications Pub Date : 1900-01-01 DOI: 10.1201/b17320-22

Sinno Jialin Pan

引用次数: 0

Uncertain Data Classification 不确定数据分类

Data Classification: Algorithms and Applications Pub Date : 1900-01-01 DOI: 10.1201/b17320-17

Reynold Cheng, Yixiang Fang, M. Renz

引用次数: 1

Instance-Based Learning: A Survey 基于实例的学习:调查

Data Classification: Algorithms and Applications Pub Date : 1900-01-01 DOI: 10.1201/b17320-7

C. Aggarwal

引用次数: 16