Combining classifiers based on minimization of a Bayes error rate

Hee Joong Kang, Seong Whan Lee

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

23 Citations (Scopus)

Abstract

In order to raise a class discrimination power by combining multiple classifiers, the upper bound of a Bayes error rate bounded by the conditional entropy of a class variable and decision variables should be minimized. Wang and Wong (1979) proposed a tree dependence approximation scheme of a high order probability distribution composed of those variables, based on minimizing the upper bound. In addition to that, this paper presents an extended approximation scheme dealing with higher order dependency. Multiple classifiers recognizing unconstrained handwritten numerals were combined by the proposed approximation scheme based on the minimization of the Bayes error rate, and the high recognition rates were obtained by them.

Original languageEnglish
Title of host publicationProceedings of the 5th International Conference on Document Analysis and Recognition, ICDAR 1999
PublisherIEEE Computer Society
Pages398-401
Number of pages4
ISBN (Electronic)0769503187
DOIs
Publication statusPublished - 1999
Event5th International Conference on Document Analysis and Recognition, ICDAR 1999 - Bangalore, India
Duration: 1999 Sep 201999 Sep 22

Publication series

NameProceedings of the International Conference on Document Analysis and Recognition, ICDAR
ISSN (Print)1520-5363

Other

Other5th International Conference on Document Analysis and Recognition, ICDAR 1999
CountryIndia
CityBangalore
Period99/9/2099/9/22

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Fingerprint Dive into the research topics of 'Combining classifiers based on minimization of a Bayes error rate'. Together they form a unique fingerprint.

Cite this