An SMO Approach to fast SVM for classification of large scale data

Juanxi Lin, Mengnan Song, Takayuki Furuzuki

研究成果: Conference contribution

抜粋

In this paper, a novel approach is proposed as a new fast Support Vector Machines (SVM) basing on sequential minimal optimization(SMO), minimum enclosing ball(MEB) approach and active set strategy. The combination with these 3 techniques largely accelerates the training process of SVM, attains fewer support vectors(SVs) as well as obtains a acceptable accuracy comparing to original SVM. From simulation results, it is stated that the proposed method will be a good alternative for classification of large scale data.

元の言語English
ホスト出版物のタイトル2014 International Conference on IT Convergence and Security, ICITCS 2014
出版者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781479965410
DOI
出版物ステータスPublished - 2014 1 23
イベント4th 2014 International Conference on IT Convergence and Security, ICITCS 2014 - Beijing, China
継続期間: 2014 10 282014 10 30

Other

Other4th 2014 International Conference on IT Convergence and Security, ICITCS 2014
China
Beijing
期間14/10/2814/10/30

ASJC Scopus subject areas

  • Computer Networks and Communications

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  • これを引用

    Lin, J., Song, M., & Furuzuki, T. (2014). An SMO Approach to fast SVM for classification of large scale data. : 2014 International Conference on IT Convergence and Security, ICITCS 2014 [7021735] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICITCS.2014.7021735