Two-stage incremental working set selection for fast support vector training on large datasets

Duc Dung Nguyen, Kazunori Matsumoto, Yasuhiro Takishima, Kazuo Hashimoto, Masahiro Terabe

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

We propose iSVM - an incremental algorithm that achieves high speed in training support vector machines (SVMs) on large datasets. In the common decomposition framework, iSVM starts with a minimum working set (WS), and then iteratively selects one training example to update the WS in each optimization loop. iSVM employs a two-stage strategy in processing the training data. In the first stage, the most prominent vector among randomly sampled data is added to the WS. This stage results in an approximate SVM solution. The second stage uses temporal solutions to scan through the whole training data once again to find the remaining support vectors (SVs). We show that iSVM is especially efficient for training SVMs on applications where data size is much larger than number of SVs. On the KDD-CUP 1999 network intrusion detection dataset with nearly five millions training examples, iSVM takes less than one hour to train an SVM with 94% testing accuracy, compared to seven hours with LibSVM - one of the state-of-the-art SVM implementations. We also provide analysis and experimental comparisons between iSVM and the related algorithms.

元の言語English
ホスト出版物のタイトルRIVF 2008 - 2008 IEEE International Conference on Research, Innovation and Vision for the Future in Computing and Communication Technologies
ページ221-226
ページ数6
DOI
出版物ステータスPublished - 2008 9 23
外部発表Yes
イベントRIVF 2008 - 2008 IEEE International Conference on Research, Innovation and Vision for the Future in Computing and Communication Technologies - Ho Chi Minh City, Viet Nam
継続期間: 2008 7 132008 7 17

出版物シリーズ

名前RIVF 2008 - 2008 IEEE International Conference on Research, Innovation and Vision for the Future in Computing and Communication Technologies

Conference

ConferenceRIVF 2008 - 2008 IEEE International Conference on Research, Innovation and Vision for the Future in Computing and Communication Technologies
Viet Nam
Ho Chi Minh City
期間08/7/1308/7/17

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition

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