We extend Support Vector Machines to input spaces that are sets by ensuring that the
classifier is invariant to permutations of sub-elements within each input. Such permutations …

Some important geometric properties of Support Vector Machines (SVM) have been studied
in the last few years, allowing researchers to develop several algorithmic aproaches to the …

We propose a learning algorithm for classification learning based on the support vector
machine (SVM) approach. Existing approaches for constructing SVMs are based on …

Leading classification methods such as support vector machines (SVMs) and their
counterparts achieve strong generalization performance by maximizing the margin of …

Abstract In classification problems, Support Vector Machines maximize the margin of
separation between two classes. While the paradigm has been successful, the solution …

Abstract We consider Support Vector Machines (SVMs) learned together with linear
transformations of the feature spaces on which they are applied. Under this scenario the …

This paper discusses classification using support vector machines in a normalized feature
space. We consider both normalization in input space and in feature space. Exploiting the …

Abstract Support Vector Machines (SVMs) are among the most fundamental tools for binary
classification. In its simplest formulation, an SVM produces a hyperplane separating two …

The aim of this paper is to afford classification tasks on asymmetric kernel matrices using
Support Vector Machines (SVMs). Ordinary theory for SVMs requires to work with symmetric …

This paper proposes a new classifier called density-induced margin support vector
machines (DMSVMs). DMSVMs belong to a family of SVM-like classifiers. Thus, DMSVMs …