ϵ-Tube based pattern selection for support vector machines

Authors:
Dongil Kim;Sungzoon Cho
Affiliations:
Department of Industrial Engineering, College of Engineering, Seoul National University, Seoul, South Korea;Department of Industrial Engineering, College of Engineering, Seoul National University, Seoul, South Korea
Venue:
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2006

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Abstract

The training time complexity of Support Vector Regression (SVR) is O(N3). Hence, it takes long time to train a large dataset. In this paper, we propose a pattern selection method to reduce the training time of SVR. With multiple bootstrap samples, we estimate ε-tube. Probabilities are computed for each pattern to fall inside ε-tube. Those patterns with higher probabilities are selected stochastically. To evaluate the new method, the experiments for 4 datasets have been done. The proposed method resulted in the best performance among all methods, and even its performance was found stable.