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CLC number: TP183
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
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SVD-LSSVM and its application in chemical pattern classification
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TAO Shao-hui, CHEN De-zhao, HU Wang-ming. SVD-LSSVM and its application in chemical pattern classification[J]. Journal of Zhejiang University Science A, 2006, 7(11): 1942-1947.
@article{title="SVD-LSSVM and its application in chemical pattern classification",
author="TAO Shao-hui, CHEN De-zhao, HU Wang-ming",
journal="Journal of Zhejiang University Science A",
volume="7",
number="11",
pages="1942-1947",
year="2006",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2006.A1942"
}
%0 Journal Article
%T SVD-LSSVM and its application in chemical pattern classification
%A TAO Shao-hui
%A CHEN De-zhao
%A HU Wang-ming
%J Journal of Zhejiang University SCIENCE A
%V 7
%N 11
%P 1942-1947
%@ 1673-565X
%D 2006
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2006.A1942
TY - JOUR
T1 - SVD-LSSVM and its application in chemical pattern classification
A1 - TAO Shao-hui
A1 - CHEN De-zhao
A1 - HU Wang-ming
J0 - Journal of Zhejiang University Science A
VL - 7
IS - 11
SP - 1942
EP - 1947
%@ 1673-565X
Y1 - 2006
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2006.A1942
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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