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Randomized kernel methods for least-squares support vector machines

Author

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  • M. Andrecut

    (Calgary, Alberta, Canada T3G 5Y8, Canada)

Abstract

The least-squares support vector machine (LS-SVM) is a frequently used kernel method for non-linear regression and classification tasks. Here we discuss several approximation algorithms for the LS-SVM classifier. The proposed methods are based on randomized block kernel matrices, and we show that they provide good accuracy and reliable scaling for multi-class classification problems with relatively large data sets. Also, we present several numerical experiments that illustrate the practical applicability of the proposed methods.

Suggested Citation

  • M. Andrecut, 2017. "Randomized kernel methods for least-squares support vector machines," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 28(02), pages 1-18, February.
    • Handle: RePEc:wsi:ijmpcx:v:28:y:2017:i:02:n:s0129183117500152
    DOI: 10.1142/S0129183117500152
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