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

Juanxi Lin, Mengnan Song, Takayuki Furuzuki

研究成果: Conference contribution

抄録

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

Fingerprint

Support vector machines

ASJC Scopus subject areas

  • Computer Networks and Communications

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

An SMO Approach to fast SVM for classification of large scale data. / Lin, Juanxi; Song, Mengnan; Furuzuki, Takayuki.

2014 International Conference on IT Convergence and Security, ICITCS 2014. Institute of Electrical and Electronics Engineers Inc., 2014. 7021735.

研究成果: Conference contribution

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., 4th 2014 International Conference on IT Convergence and Security, ICITCS 2014, Beijing, China, 14/10/28. https://doi.org/10.1109/ICITCS.2014.7021735
Lin J, Song M, Furuzuki T. An SMO Approach to fast SVM for classification of large scale data. : 2014 International Conference on IT Convergence and Security, ICITCS 2014. Institute of Electrical and Electronics Engineers Inc. 2014. 7021735 https://doi.org/10.1109/ICITCS.2014.7021735
Lin, Juanxi ; Song, Mengnan ; Furuzuki, Takayuki. / An SMO Approach to fast SVM for classification of large scale data. 2014 International Conference on IT Convergence and Security, ICITCS 2014. Institute of Electrical and Electronics Engineers Inc., 2014.
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