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Bootstrap based pattern selection for support vector regression

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Authors
Kim, Dongil; Cho, Sungzoon
Issue Date
2008-05-11
Publisher
Springer Verlag
Citation
Lecture Notes in Computer Science, Vol. 5012/2008 (2008) 608-615
Keywords
pattern selectionsupport vector regression
Abstract
Support Vector Machine (SVM) results in a good generalization performance by employing the Structural Risk Minimization (SRM)
principle. However, one drawback is O(n3) training time complexity. In
this paper, we propose a pattern selection method designed specifically
for Support Vector Regression (SVR). In SVR training, only a few patterns
called support vectors are used to construct the regression model
while other patterns are not used at all. The proposed method tries to select
patterns which are likely to become support vectors. With multiple
bootstrap samples, we estimate the likelihood of each pattern to become
a support vector. The proposed method automatically determines
the appropriate number of patterns selected by estimating the expected
number of support vectors. Through the experiments involving twenty datasets, the proposed method resulted in the best accuracy among the competing methods.
ISSN
0302-9743 (print)
1611-3349 (online)
Language
English
URI
http://hdl.handle.net/10371/6281
DOI
https://doi.org/10.1007/978-3-540-68125-0_56
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