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COMBINATION OF MULTIPLE CLASSIFIERS BY MINIMIZING THE UPPER BOUND OF BAYES ERROR RATE FOR UNCONSTRAINED HANDWRITTEN NUMERAL RECOGNITION.

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Title: COMBINATION OF MULTIPLE CLASSIFIERS BY MINIMIZING THE UPPER BOUND OF BAYES ERROR RATE FOR UNCONSTRAINED HANDWRITTEN NUMERAL RECOGNITION.
Authors: Hee-Joong Kang1 hjkang@hansung.ac.kr, Seong-Whan Lee2 swlee@image.korea.ac.kr
Source: International Journal of Pattern Recognition & Artificial Intelligence. May2005, Vol. 19 Issue 3, p395-413. 19p.
Subjects: Bayesian analysis, Entropy (Information theory), Information theory, Distribution (Probability theory), Approximation theory, Computer engineering
Abstract: In order to raise a class discrimination power by the combination of multiple classifiers, the upper bound of Bayes error rate which is bounded by the conditional entropy of a class and decisions should be minimized. Based on the minimization of the upper bound of the Bayes error rate, Wang and Wong proposed only a tree dependence approximation scheme of a high-dimensional probability distribution composed of a class and patterns. This paper extends such a tree dependence approximation scheme to higher order dependency for improving the classification performance and thus optimally approximates the high-dimensional probability distribution with a product of low-dimensional distributions. And then, a new combination method by the proposed approximation scheme is presented and evaluated with classifiers recognizing unconstrained handwritten numerals. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Pattern Recognition & Artificial Intelligence is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 16967089
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  Data: COMBINATION OF MULTIPLE CLASSIFIERS BY MINIMIZING THE UPPER BOUND OF BAYES ERROR RATE FOR UNCONSTRAINED HANDWRITTEN NUMERAL RECOGNITION.
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  Data: <searchLink fieldCode="AR" term="%22Hee-Joong+Kang%22">Hee-Joong Kang</searchLink><relatesTo>1</relatesTo><i> hjkang@hansung.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Seong-Whan+Lee%22">Seong-Whan Lee</searchLink><relatesTo>2</relatesTo><i> swlee@image.korea.ac.kr</i>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Pattern+Recognition+%26+Artificial+Intelligence%22">International Journal of Pattern Recognition & Artificial Intelligence</searchLink>. May2005, Vol. 19 Issue 3, p395-413. 19p.
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  Data: <searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Entropy+%28Information+theory%29%22">Entropy (Information theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Information+theory%22">Information theory</searchLink><br /><searchLink fieldCode="DE" term="%22Distribution+%28Probability+theory%29%22">Distribution (Probability theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Approximation+theory%22">Approximation theory</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+engineering%22">Computer engineering</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: In order to raise a class discrimination power by the combination of multiple classifiers, the upper bound of Bayes error rate which is bounded by the conditional entropy of a class and decisions should be minimized. Based on the minimization of the upper bound of the Bayes error rate, Wang and Wong proposed only a tree dependence approximation scheme of a high-dimensional probability distribution composed of a class and patterns. This paper extends such a tree dependence approximation scheme to higher order dependency for improving the classification performance and thus optimally approximates the high-dimensional probability distribution with a product of low-dimensional distributions. And then, a new combination method by the proposed approximation scheme is presented and evaluated with classifiers recognizing unconstrained handwritten numerals. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Pattern Recognition & Artificial Intelligence is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1142/S0218001405004101
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      – Code: eng
        Text: English
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      – SubjectFull: Bayesian analysis
        Type: general
      – SubjectFull: Entropy (Information theory)
        Type: general
      – SubjectFull: Information theory
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      – SubjectFull: Distribution (Probability theory)
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      – SubjectFull: Approximation theory
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      – SubjectFull: Computer engineering
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      – TitleFull: COMBINATION OF MULTIPLE CLASSIFIERS BY MINIMIZING THE UPPER BOUND OF BAYES ERROR RATE FOR UNCONSTRAINED HANDWRITTEN NUMERAL RECOGNITION.
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              M: 05
              Text: May2005
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              Y: 2005
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            – TitleFull: International Journal of Pattern Recognition & Artificial Intelligence
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