Incremental learning algorithm for support vector data description

被引：30
|
作者：
Hua X. [1 ,2 ]
Ding S. [1 ,3 ]
机构：
[1] School of Computer Science and Technology, China University of Mining and Technology, Xuzhou
[2] School of Information Engineering, Yancheng Institute of Technology, Yancheng
[3] Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing
来源：
Journal of Software | 2011年 / 6卷 / 07期
关键词：
Incremental learning; Karush-Kuhn-Tucker condition; Support vector data description;
D O I：
10.4304/jsw.6.7.1166-1173
中图分类号：
学科分类号：
摘要：
Support vector data description (SVDD) has become a very attractive kernel method due to its good results in many novelty detection problems.Training SVDD involves solving a constrained convex quadratic programming,which requires large memory and enormous amounts of training time for large-scale data set.In this paper,we analyze the possible changes of support vector set after new samples are added to training set according to the relationship between the Karush-Kuhn-Tucker (KKT) conditions of SVDD and the distribution of the training samples.Based on the analysis result,a novel algorithm for SVDD incremental learning is proposed.In this algorithm,the useless sample is discarded and useful information in training samples is accumulated.Experimental results indicate the effectiveness of the proposed algorithm. © 2011 ACADEMY PUBLISHER.
引用
收藏
页码：1166 / 1173
页数：7
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