Multi-class support vector machine simplification

Ducdung Nguyen*, Kazunori Matsumoto, Kazuo Hashimoto, Yasuhiro Takishima, Daichi Takatori, Masahiro Terabe

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


In support vector learning, computational complexity of testing phase scales linearly with number of support vectors (SVs) included in the solution - support vector machine (SVM). Among different approaches, reduced set methods speed-up the testing phase by replacing original SVM with a simplified one that consists of smaller number of SVs, called reduced vectors (RV). In this paper we introduce an extension of the bottom-up method for binary-class SVMs to multi-class SVMs. The extension includes: calculations for optimally combining two multi-weighted SVs, selection heuristic for choosing a good pair of SVs for replacing them with a newly created vector, and algorithm for reducing the number of SVs included in a SVM classifier. We show that our method possesses key advantages over others in terms of applicability, efficiency and stability. In constructing RVs, it requires finding a single maximum point of a one-variable function. Experimental results on public datasets show that simplified SVMs can run faster original SVMs up to 100 times with almost no change in predictive accuracy.

Original languageEnglish
Title of host publicationPRICAI 2008
Subtitle of host publicationTrends in Artificial Intelligence - 10th Pacific Rim International Conference on Artificial Intelligence, Proceedings
Number of pages10
Publication statusPublished - 2008
Externally publishedYes
Event10th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2008 - Hanoi, Viet Nam
Duration: 2008 Dec 152008 Dec 19

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5351 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference10th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2008
Country/TerritoryViet Nam


  • Kernel-based methods
  • Reduced set method
  • Support vector machines

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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