ε-tube based pattern selection for support vector machines

被引：0

作者：

Kim, Dongil ^{[1
]}

Cho, Sungzoon ^{[1
]}

机构：

[1] Seoul Natl Univ, Coll Engn, Dept Ind Engn, Seoul 151744, South Korea

来源：

ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS | 2006年 / 3918卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The training time complexity of Support Vector Regression (SVR) is O(N-3). 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 epsilon-tube. Probabilities are computed for each pattern to fall inside epsilon-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.

引用

页码：215 / 224

页数：10

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