NDLI: Fast training of support vector machines using top-down kernel clustering

Content Provider	IEEE Xplore Digital Library
Author	Xiao-Zhang Liu Hui-Zhen Qiu
Copyright Year	2008
Description	Author affiliation: Normal Sch. Heyuan Polytech., Heyuan, China (Xiao-Zhang Liu) \|\| Sch. of Manage., Guangdong Univ. of Bus. Studies, Guangzhou, China (Hui-Zhen Qiu)
Abstract	How to deal with the very large database in decision-making applications is a very important issue, which sometimes can be addressed using SVMs. This paper presents a new sample reduction algorithm as a sampling preprocessing for SVM training to improve the scalability. We develop a novel top-down kernel clustering approach which tends to fast produce balanced clusters of similar sizes in the kernel space. Owing to this kernel clustering step, the proposed algorithm proves efficient and effective for reducing training samples for nonlinear SVMs. Experimental results on four UCI real data benchmarks show that, with very short sampling time, the proposed sample reduction algorithm dramatically accelerates SVM training while maintaining high test accuracy.
Starting Page	968
Ending Page	971
File Size	192723
Page Count	4
File Format	PDF
ISBN	9781424421961
DOI	10.1109/ISKE.2008.4731069
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-11-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Knowledge engineering Databases Scalability Clustering algorithms Support vector machine classification Quadratic programming Kernel Management training Intelligent systems
Content Type	Text
Resource Type	Article

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