Randomized Kernel Methods for Least-Squares Support Vector Machines

M. Andrecut

doi:10.1142/s0129183117500152

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arXiv · 1703.07830

Randomized Kernel Methods for Least-Squares Support Vector Machines

M. Andrecut

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Abstract

The least-squares support vector machine is a frequently used kernel method for non-linear regression and classification tasks. Here we discuss several approximation algorithms for the least-squares support vector machine 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.

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BibTeX RIS

M. Andrecut. 2017-03-22. Randomized Kernel Methods for Least-Squares Support Vector Machines. https://doi.org/10.1142/s0129183117500152

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