A fast SVM training method for very large datasets

Boyang Li*, Qiangwei Wang, Jinglu Hu

*この研究の対応する著者

研究成果: Conference contribution

27 被引用数 (Scopus)

抄録

In a standard support vector machine (SVM), the training process has O(n3) time and O(n2) space complexities, where n is the size of training dataset. Thus, it is computationally infeasible for very large datasets. Reducing the size of training dataset is naturally considered to solve this problem. SVM classifiers depend on only support vectors (SVs) that lie close to the separation boundary. Therefore, we need to reserve the samples that are likely to be SVs. In this paper, we propose a method based on the edge detection technique to detect these samples. To preserve the entire distribution properties, we also use a clustering algorithm such as K-means to calculate the centroids of clusters. The samples selected by edge detector and the centroids of clusters are used to reconstruct the training dataset. The reconstructed training dataset with a smaller size makes the training process much faster, but without degrading the classification accuracies.

本文言語English
ホスト出版物のタイトル2009 International Joint Conference on Neural Networks, IJCNN 2009
ページ1784-1789
ページ数6
DOI
出版ステータスPublished - 2009 11月 20
イベント2009 International Joint Conference on Neural Networks, IJCNN 2009 - Atlanta, GA, United States
継続期間: 2009 6月 142009 6月 19

出版物シリーズ

名前Proceedings of the International Joint Conference on Neural Networks

Conference

Conference2009 International Joint Conference on Neural Networks, IJCNN 2009
国/地域United States
CityAtlanta, GA
Period09/6/1409/6/19

ASJC Scopus subject areas

  • ソフトウェア
  • 人工知能

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