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Online Support Vector Machine Based on Convex Hull Vertices Selection

文献类型：期刊论文

作者Wang, Di1; Qiao, Hong2; Zhang, Bo3,4; Wang, Min2
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2013-04-01
卷号24期号:4页码:593-609
关键词Kernel machine learning online classifier samples selection support vector machine
英文摘要The support vector machine (SVM) method, as a promising classification technique, has been widely used in various fields due to its high efficiency. However, SVM cannot effectively solve online classification problems since, when a new sample is misclassified, the classifier has to be retrained with all training samples plus the new sample, which is time consuming. According to the geometric characteristics of SVM, in this paper we propose an online SVM classifier called VS-OSVM, which is based on convex hull vertices selection within each class. The VS-OSVM algorithm has two steps: 1) the samples selection process, in which a small number of skeleton samples constituting an approximate convex hull in each class of the current training samples are selected and 2) the online updating process, in which the classifier is updated with newly arriving samples and the selected skeleton samples. From the theoretical point of view, the first d + 1 (d is the dimension of the input samples) selected samples are proved to be vertices of the convex hull. This guarantees that the selected samples in our approach keep the greatest amount of information of the convex hull. From the application point of view, the new algorithm can update the classifier without reducing its classification performance. Experimental results on benchmark data sets have shown the validity and effectiveness of the VS-OSVM algorithm.
WOS标题词Science & Technology ; Technology
类目[WOS]Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
研究领域[WOS]Computer Science ; Engineering
关键词[WOS]LARGE DATA SETS ; DECOMPOSITION METHODS ; TRAINING ALGORITHM ; SMO ALGORITHM ; SVM ; CONVERGENCE ; MODEL ; CLASSIFIERS ; REDUCTION
收录类别SCI
语种英语
WOS记录号WOS:000315517400008
源URL[http://ir.ia.ac.cn/handle/173211/3030]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组
作者单位1.Wenzhou Univ, Coll Math & Informat Sci, Wenzhou 325035, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
3.Chinese Acad Sci, LSEC, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Inst Appl Math, AMSS, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Di,Qiao, Hong,Zhang, Bo,et al. Online Support Vector Machine Based on Convex Hull Vertices Selection[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2013,24(4):593-609.
APA Wang, Di,Qiao, Hong,Zhang, Bo,&Wang, Min.(2013).Online Support Vector Machine Based on Convex Hull Vertices Selection.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,24(4),593-609.
MLA Wang, Di,et al."Online Support Vector Machine Based on Convex Hull Vertices Selection".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 24.4(2013):593-609.

入库方式： OAI收割

来源：自动化研究所

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