Bootstrap based pattern selection for support vector regression

Dongil Kim, Sungzoon Cho

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations


Support Vector Machine (SVM) results in a good generalization performance by employing the Structural Risk Minimization (SRM) principle. However, one drawback is O(n 3) 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.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 12th Pacific-Asia Conference, PAKDD 2008, Proceedings
Number of pages8
StatePublished - 2008
Event12th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2008 - Osaka, Japan
Duration: 20 May 200823 May 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5012 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference12th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2008


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