Randomized kernel methods for least-squares support vector machines

M. Andrecut ()
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M. Andrecut: Calgary, Alberta, Canada T3G 5Y8, Canada

International Journal of Modern Physics C (IJMPC), 2017, vol. 28, issue 02, 1-18

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.

Keywords: Kernel methods; multiclass classification; big data sets (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1142/S0129183117500152

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