This paper provides an overview of the support vector machine (SVM) methodology and its
applicability to real‐world engineering problems. Specifically, the aim of this study is to …

The present paper is Part 2 in this series of two papers. In Part 1 we provided an introduction
to Multiple Classifier Systems (MCS) with a focus into the fundamentals: basic nomenclature …

In this paper we apply a recently proposed technique of optimal control by support vector
machines (SVMs) to chaos control. Vapnik's support vector method, which is based on the …

Since the introduction of support vector machines, we have witnessed the huge
development in theory, models, and applications of what is so-called kernel-based methods …

Abstract In Support Vector Machines (SVMs), the solution of the classification problem is
characterized by a (convex) quadratic programming (QP) problem. In a modified version of …

The geometric framework for the support vector machine (SVM) classification problem
provides an intuitive ground for the understanding and the application of geometric …

In the last few years, the support vector machine (SVM) method has motivated new interest
in kernel regression techniques. Although the SVM has been shown to exhibit excellent …

A realistic and comprehensive review of joint approaches to machine learning and signal
processing algorithms, with application to communications, multimedia, and biomedical …

The combination of the famed kernel trick and affine projection algorithms (APAs) yields
powerful nonlinear extensions, named collectively here, KAPA. This paper is a follow-up …

This paper presents a new approach to auto-regressive and moving average (ARMA)
modeling based on the support vector method (SVM) for identification applications. A …