Asymmetric Gaussian and its application to pattern recognition

Tsuyoshi Kato, Shinichiro Omachi, Hirotomo Aso

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

35 Citations (Scopus)

Abstract

In this paper, we propose a new probability model, ‘asymmetric Gaussian(AG),’ which can capture spatially asymmetric distributions. It is also extended to mixture of AGs. The values of its parameters can be determined by Expectation-Conditional Maximization algorithm. We apply the AGs to a pattern classification problem and show that the AGs outperform Gaussian models.

Original languageEnglish
Title of host publicationStructural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshops SSPR 2002 and SPR 2002, Proceedings
EditorsTerry Caelli, Adnan Amin, Robert P.W. Duin, Dick de Ridder, Mohamed Kamel
PublisherSpringer Verlag
Pages405-413
Number of pages9
ISBN (Print)3540440119, 9783540440116
DOIs
Publication statusPublished - 2002
EventJoint IAPR 9th International Workshop on Structural and Syntactic Pattern Recognition, SSPR 2002 and 4th International Workshop on Statistical Techniques in Pattern Recognition, SPR 2002 - Windsor, Canada
Duration: 2002 Aug 62002 Aug 9

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2396
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherJoint IAPR 9th International Workshop on Structural and Syntactic Pattern Recognition, SSPR 2002 and 4th International Workshop on Statistical Techniques in Pattern Recognition, SPR 2002
CountryCanada
CityWindsor
Period02/8/602/8/9

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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