Online Support Vector Machine Based on Convex Hull Vertices Selection
Wang, Di1; Qiao, Hong2; Zhang, Bo3,4; Wang, Min2
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
2013-04-01
Volume24Issue:4Pages:593-609
SubtypeArticle
AbstractThe 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.
KeywordKernel Machine Learning Online Classifier Samples Selection Support Vector Machine
WOS HeadingsScience & Technology ; Technology
WOS KeywordLARGE DATA SETS ; DECOMPOSITION METHODS ; TRAINING ALGORITHM ; SMO ALGORITHM ; SVM ; CONVERGENCE ; MODEL ; CLASSIFIERS ; REDUCTION
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000315517400008
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/3030
Collection复杂系统管理与控制国家重点实验室_机器人理论与应用
Affiliation1.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
Recommended Citation
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.
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