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| SVD-LSSVM and its application in chemical pattern classification |
| TAO Shao-hui, CHEN De-zhao, HU Wang-ming |
| Department of Chemical Engineering, Zhejiang University, Hangzhou 310027, China |
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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.
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Received: 28 May 2006
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