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 …

SVM training is usually discussed under two different algorithmic points of view. The first one
is provided by decomposition methods such as SMO and SVMLight while the second one …

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 …

We give a new proof of the convergence of the SMO algorithm for SVM training over linearly
separable problems that partly builds on the one by Mitchell et al. for the convergence of the …

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 …

We propose an improved version of the SMO algorithm for training classification and
regression SVMs, based on a Conjugate Descent procedure. This new approach only …

The training time for Support vector machine (SVM) depends largely on the size of the
training set, which makes it impractical for large data sets. This paper presents a new …

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 …

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 …