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On-line Access: 2024-08-27

Received: 2023-10-17

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Journal of Zhejiang University SCIENCE A 2006 Vol.7 No.11 P.1942-1947

http://doi.org/10.1631/jzus.2006.A1942


SVD-LSSVM and its application in chemical pattern classification


Author(s):  TAO Shao-hui, CHEN De-zhao, HU Wang-ming

Affiliation(s):  Department of Chemical Engineering, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   dzc@zju.edu.cn

Key Words:  Pattern classification, Structural risk minimization, Least squares support vector machine (LSSVM), Hyper parameter selection, Cross validation, Singular value decomposition (SVD)


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.

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author="TAO Shao-hui, CHEN De-zhao, HU Wang-ming",
journal="Journal of Zhejiang University Science A",
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pages="1942-1947",
year="2006",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2006.A1942"
}

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%T SVD-LSSVM and its application in chemical pattern classification
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%A HU Wang-ming
%J Journal of Zhejiang University SCIENCE A
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%N 11
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%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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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[6] Suykens, J.A.K., Vandewalle, J., 1999a. Least squares support vector machine classifiers. Neural Processing Letters, 9(3):293-300.

[7] Suykens, J.A.K., Vandewalle, J., 1999b. Multiclass Least Squares Support Vector Machines. Intl. Joint Conference on Neural Networks, IJCNN’99, Washington, D.C.

[8] van Gestel, T., Suykens, J.A.K., Baesens, B., Stijn, V., Vanthienen, J., Dedene, G., Bart, D.M., Vandewalle, J., 2004. Benchmarking least squares support vector machine classifiers. Machine Learning, 54(1):5-32.

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