A Note on Least Squares Support Vector Machines
نویسندگان
چکیده
In this paper, we propose some improvements for the implementations of least squares support vector machine classifiers (LS-SVM). An improved conjugate gradient scheme is proposed for solving the optimization problems in LS-SVM, and an improved SMO algorithm is put forward for the general unconstrained quadratic programming problems which is the case of LS-SVM without the bias term. Numerical experiments are carried out to verify the usefulness of these improvements. We also attempt to point out the potential weaknesses in Bayesian framework for LS-SVM classifiers proposed by Van Gestel et al. (2002).
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