Various hyperplane classifiers using kernel feature spaces

Kovács, Kornél and Kocsor, András: Various hyperplane classifiers using kernel feature spaces. Acta cybernetica, (16) 2. pp. 271-278. (2003)

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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.

Item Type: Article
Event Title: Conference for PhD Students in Computer Science, 3., 2002, Szeged
Journal or Publication Title: Acta cybernetica
Date: 2003
Volume: 16
Number: 2
Page Range: pp. 271-278
ISSN: 0324-721X
Language: angol
Uncontrolled Keywords: Természettudomány, Informatika
Additional Information: Bibliogr.: 278. p.; Abstract
Date Deposited: 2016. Oct. 15. 12:25
Last Modified: 2018. Apr. 10. 16:12
URI: http://acta.bibl.u-szeged.hu/id/eprint/12722

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