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Article: Learning templates from fuzzy examples in structural pattern recognition

TitleLearning templates from fuzzy examples in structural pattern recognition
Authors
Issue Date1996
Citation
Ieee Transactions On Systems, Man, And Cybernetics, Part B: Cybernetics, 1996, v. 26 n. 1, p. 118-123 How to Cite?
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
2014 Impact Factor: 6.22
2015 SCImago Journal Rankings: 3.921
References

 

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.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.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1109/3477.484443en_US
dc.identifier.scopuseid_2-s2.0-0030083928en_US
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.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridChan, KP=7406032820en_US

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