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 …

Efficiently implemented active set methods have been successfully applied to support vector
machine (SVM) training. These active set methods offer higher precision and incremental …

Training of support vector machines (SVMs) requires to solve a linearly constrained convex
quadratic problem. In real applications, the number of training data may be very huge and …

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 …

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 …

Building upon Gilbert's convergence proof of his algorithtm to solve the Minimum Norm
Problem, we establish a framework where a much simplified version of his proof allows us to …

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 …

Abstract We revise Nesterov's Accelerated Gradient (NAG) procedure for the SVM dual
problem and propose a strictly monotone version of NAG that is capable of accelerating the …

Sequential minimal optimization (SMO) algorithm is one of the simplest decomposition
methods for learning of support vector machines (SVMs). Keerthi and Gilbert have recently …

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 …