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 sequential minimal optimization (SMO) algorithm is known to be one of the most efficient
solutions for the support vector machine training phase. It solves a quadratic programming …

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 …

Second order SMO represents the state–of–the–art in SVM training for moderate size
problems. In it, the solution is attained by solving a series of subproblems which are …

In this work we will propose an acceleration procedure for the Mitchell–Demyanov–
Malozemov (MDM) algorithm (a fast geometric algorithm for SVM construction) that may …

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 …

We propose a framework based on a parametric quadratic program (cid: 173) ming (QP)
technique to solve the support vector machine (SVM) training problem. This framework, can …

In this work we will give explicit formulae for the application of Rosen's gradient projection
method to SVM training that leads to a very simple implementation. We shall experimentally …

In a recently published paper in JMLR, Tsang et al.(2005) present an algorithm for SVM
called Core Vector Machines (CVM) and illustrate its performances through comparisons …

Sequential Minimal Optimization (SMO) can be regarded as the state-of-the-art approach in
non-linear Support Vector Machines training, being the method of choice in the successful …