Multi-class support vector machine simplification

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

研究成果: Conference contribution

2 引用 (Scopus)

抜粋

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.

元の言語English
ホスト出版物のタイトルPRICAI 2008
ホスト出版物のサブタイトルTrends in Artificial Intelligence - 10th Pacific Rim International Conference on Artificial Intelligence, Proceedings
ページ799-808
ページ数10
DOI
出版物ステータスPublished - 2008 12 1
イベント10th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2008 - Hanoi, Viet Nam
継続期間: 2008 12 152008 12 19

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
5351 LNAI
ISSN(印刷物)0302-9743
ISSN(電子版)1611-3349

Conference

Conference10th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2008
Viet Nam
Hanoi
期間08/12/1508/12/19

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • これを引用

    Nguyen, D., Matsumoto, K., Hashimoto, K., Takishima, Y., Takatori, D., & Terabe, M. (2008). Multi-class support vector machine simplification. : PRICAI 2008: Trends in Artificial Intelligence - 10th Pacific Rim International Conference on Artificial Intelligence, Proceedings (pp. 799-808). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 巻数 5351 LNAI). https://doi.org/10.1007/978-3-540-89197-0_74