Adaptive classifier construction: An approach to handwritten digit recognition
Identifieur interne : 000636 ( PascalFrancis/Checkpoint ); précédent : 000635; suivant : 000637Adaptive classifier construction: An approach to handwritten digit recognition
Auteurs : TUAN TRUNG NGUYEN [Pologne]Source :
- Lecture notes in computer science [ 0302-9743 ] ; 2002.
Descripteurs français
- Pascal (Inist)
- Wicri :
- topic : Classification, Intelligence artificielle.
English descriptors
- KwdEn :
Abstract
Optical Character Recognition (OCR) is a classic example of decision making problem where class identities of image objects are to be determined. This concerns essentially of finding a decision function that returns the correct classification of input objects. This paper proposes a method of constructing such functions using an adaptive learning framework, which comprises of a multilevel classifier synthesis schema. The schema's structure and the way classifiers on a higher level are synthesized from those on lower levels are subject to an adaptive iterative process that allows to learn from the input training data. Detailed algorithms and classifiers based on similarity and dissimilarity measures are presented. Also, results of computer experiments using described techniques on a large handwritten digit database are included as an illustration of the application of proposed methods.
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Pascal:03-0151166Le document en format XML
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Koszykowa 86</s1><s2>02-008 Warsaw</s2><s3>POL</s3><sZ>1 aut.</sZ></inist:fA14><country>Pologne</country><wicri:noRegion>02-008 Warsaw</wicri:noRegion></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">INIST</idno><idno type="inist">03-0151166</idno><date when="2002">2002</date><idno type="stanalyst">PASCAL 03-0151166 INIST</idno><idno type="RBID">Pascal:03-0151166</idno><idno type="wicri:Area/PascalFrancis/Corpus">000628</idno><idno type="wicri:Area/PascalFrancis/Curation">000163</idno><idno type="wicri:Area/PascalFrancis/Checkpoint">000636</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a">Adaptive classifier construction: An approach to handwritten digit recognition</title><author><name sortKey="Tuan Trung Nguyen" sort="Tuan Trung Nguyen" uniqKey="Tuan Trung Nguyen" last="Tuan Trung Nguyen">TUAN TRUNG NGUYEN</name><affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Polish-Japanese Institute of Information Technology, ul. Koszykowa 86</s1><s2>02-008 Warsaw</s2><s3>POL</s3><sZ>1 aut.</sZ></inist:fA14><country>Pologne</country><wicri:noRegion>02-008 Warsaw</wicri:noRegion></affiliation></author></analytic><series><title level="j" type="main">Lecture notes in computer science</title><idno type="ISSN">0302-9743</idno><imprint><date when="2002">2002</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">Lecture notes in computer science</title><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Artificial intelligence</term><term>Character recognition</term><term>Classification</term><term>Cluster analysis</term><term>Decision support system</term><term>Image processing</term><term>Manuscript character</term><term>Optical character recognition</term><term>Pattern recognition</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Traitement image</term><term>Reconnaissance forme</term><term>Reconnaissance optique caractère</term><term>Caractère manuscrit</term><term>Reconnaissance caractère</term><term>Classification</term><term>Intelligence artificielle</term><term>Système aide décision</term><term>Analyse amas</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Classification</term><term>Intelligence artificielle</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Optical Character Recognition (OCR) is a classic example of decision making problem where class identities of image objects are to be determined. This concerns essentially of finding a decision function that returns the correct classification of input objects. This paper proposes a method of constructing such functions using an adaptive learning framework, which comprises of a multilevel classifier synthesis schema. The schema's structure and the way classifiers on a higher level are synthesized from those on lower levels are subject to an adaptive iterative process that allows to learn from the input training data. Detailed algorithms and classifiers based on similarity and dissimilarity measures are presented. 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