Support Vector-based learning methods are an important part of Computational Intelligence
techniques. Recent efforts have been dealing with the problem of learning from very large …

Support vector regression (SVR) and support vector classification (SVC) are popular
learning techniques, but their use with kernels is often time consuming. Recently, linear SVC …

This paper presents a decomposition method for efficiently constructing ℓ 1-norm Support
Vector Machines (SVMs). The decomposition algorithm introduced in this paper possesses …

Abstract Support Vector Machines (SVMs) have proven to be highly effective for learning
many real world datasets but have failed to establish themselves as common machine …

Support vector machine (SVM) provides good generalization performance but suffers from a
large amount of computation. This paper presents an incremental learning strategy for …

Abstract Support Vector Machines (SVMs) for regression problems are trained by solving a
quadratic optimization problem which needs on the order of l2 memory and time resources …

Rooted in statistical learning or Vapnik-Chervonenkis (VC) theory, support vector machines
(SVMs) are well positioned to generalize on yet-to-be-seen data. The SVM concepts …

In this paper we describe a novel extension of the support vector machine, called the deep
support vector machine (DSVM). The original SVM has a single layer with kernel functions …

Recently the reduced support vector machine (RSVM) was proposed as an alternate of the
standard SVM. Motivated by resolving the difficulty on handling large data sets using SVM …

A heuristic method for accelerating support vector machine (SVM) training based on a
measurement of similarity among samples is presented in this paper. To train SVM, a …