A Maximal Margin Hypersphere SVM

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this work we propose a generalization of the Support Vector Machine (SVM) method in which the separator is a curve, but the concept of margin and maximization of the margin is still present. The idea of using different functions for the separation has been explored in particular in the scope of hyperspheres. However, most of these proposals use two spheres, using concepts different from maximal margin or one sphere but with a poor performance when data from the classes have a linear shape. In this paper we present a formulation of the linear SVM that generalizes it to a spherical separation shape, but still maximizing the margin. A linear relaxation of this quadratic formulation is also presented. The performance of these two formulations for classification purpose is tested and the results are encouraging.

Original languageEnglish
Title of host publicationComputational Science and Its Applications – ICCSA 2021: 21st International Conference, Cagliari, Italy, September 13–16, 2021, Proceedings, Part V
EditorsOsvaldo Gervasi, Beniamino Murgante, Sanjay Misra, Chiara Garau, Ivan Blečić, David Taniar, Bernady O. Apduhan, Ana Maria Rocha, Eufemia Tarantino, Carmelo Maria Torre
Place of PublicationCham
PublisherSpringer
Pages304-319
Number of pages16
ISBN (Electronic)978-3-030-86976-2
ISBN (Print)978-3-030-86975-5
DOIs
Publication statusPublished - 2021
Event21st International Conference on Computational Science and Its Applications, ICCSA 2021 - Virtual, Online
Duration: 13 Sep 202116 Sep 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer
Volume12953 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21st International Conference on Computational Science and Its Applications, ICCSA 2021
CityVirtual, Online
Period13/09/2116/09/21

Keywords

  • Automatic classification
  • Non-linear SVM
  • SVM

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