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Article: Learning templates from fuzzy examples in structural pattern recognition

TitleLearning templates from fuzzy examples in structural pattern recognition
Authors
Chan, KP
Issue Date1996
Citation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 1996, v. 26 n. 1, p. 118-123 How to Cite?
DOI: http://dx.doi.org/10.1109/3477.484443
AbstractFuzzy-Attribute Graph (FAG) was proposed to handle fuzziness in the pattern primitives in structural pattern recognition [1]. FAG has the advantage that we can combine several possible definition into a single template, and hence only one matching is required instead of one for each definition. Also, each vertex or edge of the graph can contain fuzzy attributes to model real-life situation. However, in our previous approach, we need a human expert to define the templates for the fuzzy graph matching. This is usually tedious, time-consuming and error-prone. In this paper, we propose a learning algorithm that will, from a number of fuzzy examples, each of them being a FAG, find the smallest template that can be matched to the given patterns with respect to the matching metric. © 1996 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/152255
ISSN
1083-4419
2014 Impact Factor: 6.220
ISI Accession Number ID
WOS:A1996UC99900010
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorChan, KPen_US
dc.date.accessioned2012-06-26T06:36:46Z-
dc.date.available2012-06-26T06:36:46Z-
dc.date.issued1996en_US
dc.identifier.citationIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 1996, v. 26 n. 1, p. 118-123en_US
dc.identifier.issn1083-4419en_US
dc.identifier.urihttp://hdl.handle.net/10722/152255-
dc.description.abstractFuzzy-Attribute Graph (FAG) was proposed to handle fuzziness in the pattern primitives in structural pattern recognition [1]. FAG has the advantage that we can combine several possible definition into a single template, and hence only one matching is required instead of one for each definition. Also, each vertex or edge of the graph can contain fuzzy attributes to model real-life situation. However, in our previous approach, we need a human expert to define the templates for the fuzzy graph matching. This is usually tedious, time-consuming and error-prone. In this paper, we propose a learning algorithm that will, from a number of fuzzy examples, each of them being a FAG, find the smallest template that can be matched to the given patterns with respect to the matching metric. © 1996 IEEE.en_US
dc.languageengen_US
dc.relation.ispartofIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cyberneticsen_US
dc.rights©1996 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.titleLearning templates from fuzzy examples in structural pattern recognitionen_US
dc.typeArticleen_US
dc.identifier.emailChan, KP:kpchan@cs.hku.hken_US
dc.identifier.authorityChan, KP=rp00092en_US
dc.description.naturepublished_or_final_versionen_US
dc.identifier.doi10.1109/3477.484443en_US
dc.identifier.pmid18263011-
dc.identifier.scopuseid_2-s2.0-0030083928en_US
dc.identifier.hkuros14155-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0030083928&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume26en_US
dc.identifier.issue1en_US
dc.identifier.spage118en_US
dc.identifier.epage123en_US
dc.identifier.isiWOS:A1996UC99900010-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridChan, KP=7406032820en_US
dc.identifier.issnl1083-4419-

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