ε-tube based pattern selection for support vector machines

Dongil Kim, Sungzoon Cho

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

11 Scopus citations

Abstract

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 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 data-sets have been done. The proposed method resulted in the best performance among all methods, and even its performance was found stable.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 10th Pacific-Asia Conference, PAKDD 2006, Proceedings
PublisherSpringer Verlag
Pages215-224
Number of pages10
ISBN (Print)3540332065, 9783540332060
DOIs
StatePublished - 2006
Event10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006 - Singapore, Singapore
Duration: 9 Apr 200612 Apr 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3918 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006
Country/TerritorySingapore
CitySingapore
Period9/04/0612/04/06

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