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Journal of Zhejiang University SCIENCE A
ISSN 1673-565X(Print), 1862-1775(Online), Monthly
2006 Vol.7 No.11 P.1942-1947
SVD-LSSVM and its application in chemical pattern classification
Abstract: Pattern classification is an important field in machine learning; least squares support vector machine (LSSVM) is a powerful tool for pattern classification. A new version of LSSVM, SVD-LSSVM, to save time of selecting hyper parameters for LSSVM is proposed. SVD-LSSVM is trained through singular value decomposition (SVD) of kernel matrix. Cross validation time of selecting hyper parameters can be saved because a new hyper parameter, singular value contribution rate (SVCR), replaces the penalty factor of LSSVM. Several UCI benchmarking data and the Olive classification problem were used to test SVD-LSSVM. The result showed that SVD-LSSVM has good performance in classification and saves time for cross validation.
Key words: Pattern classification, Structural risk minimization, Least squares support vector machine (LSSVM), Hyper parameter selection, Cross validation, Singular value decomposition (SVD)
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Open peer comments: Debate/Discuss/Question/Opinion
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DOI:
10.1631/jzus.2006.A1942
CLC number:
TP183
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Received:
2006-05-28
Revision Accepted:
2006-07-28
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