A neuro-fuzzy architecture for high performance classification
Identifieur interne : 002A89 ( Istex/Corpus ); précédent : 002A88; suivant : 002A90A neuro-fuzzy architecture for high performance classification
Auteurs : Sung-Bae ChoSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 1995.
Abstract
Abstract: The concept of combining modular neural networks has been recently exploited as a new direction for the development of highly reliable neural network systems in the area of pattern classification. In this paper we present an efficient method for combining the modular networks based on fuzzy logic, especially the fuzzy integral. This method nonlinearly combines objective evidences, in the form of network outputs, with subjective evaluation of the reliability of the individual neural networks. Also, for more effective aggregation, we adopt the extension of the fuzzy integral with ordered weighted averaging operators. The experimental results with the recognition problem of on-line handwriting characters show that the performance of individual networks could be improved significantly.
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DOI: 10.1007/3-540-60607-6_6
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In this paper we present an efficient method for combining the modular networks based on fuzzy logic, especially the fuzzy integral. This method nonlinearly combines objective evidences, in the form of network outputs, with subjective evaluation of the reliability of the individual neural networks. Also, for more effective aggregation, we adopt the extension of the fuzzy integral with ordered weighted averaging operators. 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In this paper we present an efficient method for combining the modular networks based on fuzzy logic, especially the fuzzy integral. This method nonlinearly combines objective evidences, in the form of network outputs, with subjective evaluation of the reliability of the individual neural networks. Also, for more effective aggregation, we adopt the extension of the fuzzy integral with ordered weighted averaging operators. The experimental results with the recognition problem of on-line handwriting characters show that the performance of individual networks could be improved significantly.</Para></Abstract><ArticleNote Type="Misc"><SimplePara>This work was partly conducted while at: KAIST Computer Science Department, 373-1 Koosung-dong, Yoosung-ku, Taejeon 305–701, Korea, sbchogorai.kaist.ac.kr; and ATR Human Information Processing Research Laboratories, 2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-02, Japan, sbchohip.atr.co.jp</SimplePara></ArticleNote></ChapterHeader><NoBody></NoBody></Chapter></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>A neuro-fuzzy architecture for high performance classification</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>A neuro-fuzzy architecture for high performance classification</title></titleInfo><name type="personal"><namePart type="given">Sung-Bae</namePart><namePart type="family">Cho</namePart><affiliation>Department of Computer Science, Yonsei University, 134 Shinchon-dong, 120-749, Sudaemoon-ku, Seoul, Korea</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference [eBooks]" displayLabel="ReviewPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">1995</dateIssued><copyrightDate encoding="w3cdtf">1995</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Abstract: The concept of combining modular neural networks has been recently exploited as a new direction for the development of highly reliable neural network systems in the area of pattern classification. 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