Title: Randomized Kernel Methods for Least-Squares Support Vector Machines,
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.