While usually SVM training tries to solve the dual of the standard SVM minimization problem,
alternative algorithms that solve the Nearest Point Problem (NPP) for the convex hulls of the …

It is well known that linear slack penalty SVM training is equivalent to solving the Nearest
Point Problem (NPP) over the so-called μ-Reduced Convex Hulls, that is, convex …

Training support vector machines (SVM) consists of solving a convex quadratic problem
(QP) with one linear equality and box constraints. In this paper, we solve this QP by a primal …

Existing active set methods reported in the literature for support vector machine (SVM)
training must contend with singularities when solving for the search direction. When a …

Most literature on support vector machines (SVMs) concentrates on the dual optimization
problem. In this chapter, we would like to point out that the primal problem can also be …

Support vector machines (SVMs) are an extremely successful class of classification and
regression algorithms. Building an SVM entails the solution of a constrained convex …

SVM training is a convex optimization problem which scales with the training set size rather
than the feature space dimension. While this is usually considered to be a desired quality, in …

Fast SVM training is an important goal for which many proposals have been given in the
literature. In this work we will study from a geometrical point of view the presence, in both the …

The well-known MinOver algorithm is a slight modification of the perceptron algorithm and
provides the maximum-margin classifier without a bias in linearly separable two-class …

We introduce homogeneous coordinates to represent support vector machines (SVMs) and
develop a corresponding training algorithm: single sequential minimal optimization (SSMO) …