LS_SVMlab工具箱（附带使用说明）

1 Introduction 4

2 A birds eye view on LS-SVMlab 5

3 LS-SVMlab toolbox examples 10

A MATLAB functions 29

Support Vector Machines (SVM) is a powerful methodology for solving problems in nonlinear

classification, function estimation and density estimation which has also led to many other recent

developments in kernel based learning methods in general [3, 16, 17, 34, 33]. SVMs have been in-

troduced within the context of statistical learning theory and structural risk minimization. In the

methods one solves convex optimization problems, typically quadratic programs. Least Squares

Support Vector Machines (LS-SVM) are reformulations to standard SVMs [21, 28] which lead

to solving linear KKT systems. LS-SVMs are closely related to regularization networks [5] and

Gaussian processes [37] but additionally emphasize and exploit primal-dual interpretations. Links

between kernel versions of classical pattern recognition algorithms such as kernel Fisher discrim-

inant analysis and extensions to unsupervised learning, recurrent networks and control [22] are

ber of LS-SVM algorithms related to classification, regression, time-series prediction and unsuper-

vised learning. References to commands in the toolbox are written in typewriter font.