A kernel method for the optimization of the margin distribution

Fabio Aiolli, Giovanni Martino, Alessandro Sperduti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Citations (Scopus)

Abstract

Recent results in theoretical machine learning seem to suggest that nice properties of the margin distribution over a training set turns out in a good performance of a classifier. The same principle has been already used in SVM and other kernel based methods as the associated optimization problems try to maximize the minimum of these margins. In this paper, we propose a kernel based method for the direct optimization of the margin distribution (KM-OMD). The method is motivated and analyzed from a game theoretical perspective. A quite efficient optimization algorithm is then proposed. Experimental results over a standard benchmark of 13 datasets have clearly shown state-of-the-art performances.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages305-314
Number of pages10
Volume5163 LNCS
EditionPART 1
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event18th International Conference on Artificial Neural Networks, ICANN 2008 - Prague
Duration: 3 Sep 20086 Sep 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5163 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other18th International Conference on Artificial Neural Networks, ICANN 2008
CityPrague
Period3/9/086/9/08

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Keywords

  • Game theory
  • Kernel methods
  • Margin distribution

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Aiolli, F., Martino, G., & Sperduti, A. (2008). A kernel method for the optimization of the margin distribution. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 1 ed., Vol. 5163 LNCS, pp. 305-314). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5163 LNCS, No. PART 1). https://doi.org/10.1007/978-3-540-87536-9_32