Various hyperplane classifiers using kernel feature spaces

Kornél Kovács, András Kocsor

Research output: Contribution to journalArticle

Abstract

In this paper we introduce a new family of hyperplane classifiers. But, in contrast to Support Vector Machines (SVM) - where a constrained quadratic optimization is used - some of the proposed methods lead to the unconstrained minimization of convex functions while others merely require solving a linear system of equations. So that the efficiency of these methods could be checked, classification tests were conducted on standard databases. In our evaluation, classification results of SVM were of course used as a general point of reference, which we found were outperformed in many cases.

Original languageEnglish
Pages (from-to)271-278
Number of pages8
JournalActa Cybernetica
Volume16
Issue number2
Publication statusPublished - Jan 1 2003

    Fingerprint

ASJC Scopus subject areas

  • Software
  • Computer Science (miscellaneous)
  • Computer Vision and Pattern Recognition
  • Management Science and Operations Research
  • Information Systems and Management
  • Electrical and Electronic Engineering

Cite this