Multiclass least squares support vector machines

We present an extension of least squares support vector machines (LS-SVMs) to the multiclass case. While standard SVM solutions involve solving quadratic or linear programming problems, the least squares version of SVMs corresponds to solving a set of linear equations, due to equality instead of inequality constraints in the problem formulation. In LS-SVMs the Mercer condition is still applicable. Hence several type of kernels such as polynomial, RBFs and MLPs can be used. The multiclass case that we discuss here is related to classical neural net approaches for classification where multi-classes are encoded by considering multiple outputs for the network. Efficient methods for solving large scale LS-SVMs are available