Study of distributed learning as a solution to category proliferation in Fuzzy ARTMAP based neural systems.
Identifieur interne : 000556 ( PubMed/Checkpoint ); précédent : 000555; suivant : 000557Study of distributed learning as a solution to category proliferation in Fuzzy ARTMAP based neural systems.
Auteurs : Emilio Parrado-Hernández [Espagne] ; Eduardo G Mez-Sánchez ; Yannis A. DimitriadisSource :
- Neural networks : the official journal of the International Neural Network Society [ 0893-6080 ] ; 2003.
Descripteurs français
- KwdFr :
- MESH :
- physiologie : Apprentissage.
- Logiciel, Logique floue, Réseaux neuronaux (ordinateur).
English descriptors
- KwdEn :
- MESH :
- physiology : Learning.
- Fuzzy Logic, Neural Networks (Computer), Software.
Abstract
An evaluation of distributed learning as a means to attenuate the category proliferation problem in Fuzzy ARTMAP based neural systems is carried out, from both qualitative and quantitative points of view. The study involves two original winner-take-all (WTA) architectures, Fuzzy ARTMAP and FasArt, and their distributed versions, dARTMAP and dFasArt. A qualitative analysis of the distributed learning properties of dARTMAP is made, focusing on the new elements introduced to endow Fuzzy ARTMAP with distributed learning. In addition, a quantitative study on a selected set of classification problems points out that problems have to present certain features in their output classes in order to noticeably reduce the number of recruited categories and achieve an acceptable classification accuracy. As part of this analysis, distributed learning was successfully adapted to a member of the Fuzzy ARTMAP family, FasArt, and similar procedures can be used to extend distributed learning capabilities to other Fuzzy ARTMAP based systems.
DOI: 10.1016/S0893-6080(03)00009-1
PubMed: 14692638
Affiliations:
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Dimitriadis</name></author></analytic><series><title level="j">Neural networks : the official journal of the International Neural Network Society</title><idno type="ISSN">0893-6080</idno><imprint><date when="2003" type="published">2003</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Fuzzy Logic</term><term>Learning (physiology)</term><term>Neural Networks (Computer)</term><term>Software</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Apprentissage (physiologie)</term><term>Logiciel</term><term>Logique floue</term><term>Réseaux neuronaux (ordinateur)</term></keywords><keywords scheme="MESH" qualifier="physiologie" xml:lang="fr"><term>Apprentissage</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Learning</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Fuzzy Logic</term><term>Neural Networks (Computer)</term><term>Software</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Logiciel</term><term>Logique floue</term><term>Réseaux neuronaux (ordinateur)</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">An evaluation of distributed learning as a means to attenuate the category proliferation problem in Fuzzy ARTMAP based neural systems is carried out, from both qualitative and quantitative points of view. The study involves two original winner-take-all (WTA) architectures, Fuzzy ARTMAP and FasArt, and their distributed versions, dARTMAP and dFasArt. A qualitative analysis of the distributed learning properties of dARTMAP is made, focusing on the new elements introduced to endow Fuzzy ARTMAP with distributed learning. In addition, a quantitative study on a selected set of classification problems points out that problems have to present certain features in their output classes in order to noticeably reduce the number of recruited categories and achieve an acceptable classification accuracy. As part of this analysis, distributed learning was successfully adapted to a member of the Fuzzy ARTMAP family, FasArt, and similar procedures can be used to extend distributed learning capabilities to other Fuzzy ARTMAP based systems.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">14692638</PMID><DateCreated><Year>2003</Year><Month>12</Month><Day>24</Day></DateCreated><DateCompleted><Year>2004</Year><Month>02</Month><Day>10</Day></DateCompleted><DateRevised><Year>2006</Year><Month>11</Month><Day>15</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Print">0893-6080</ISSN><JournalIssue CitedMedium="Print"><Volume>16</Volume><Issue>7</Issue><PubDate><Year>2003</Year><Month>Sep</Month></PubDate></JournalIssue><Title>Neural networks : the official journal of the International Neural Network Society</Title><ISOAbbreviation>Neural Netw</ISOAbbreviation></Journal><ArticleTitle>Study of distributed learning as a solution to category proliferation in Fuzzy ARTMAP based neural systems.</ArticleTitle><Pagination><MedlinePgn>1039-57</MedlinePgn></Pagination><Abstract><AbstractText>An evaluation of distributed learning as a means to attenuate the category proliferation problem in Fuzzy ARTMAP based neural systems is carried out, from both qualitative and quantitative points of view. The study involves two original winner-take-all (WTA) architectures, Fuzzy ARTMAP and FasArt, and their distributed versions, dARTMAP and dFasArt. A qualitative analysis of the distributed learning properties of dARTMAP is made, focusing on the new elements introduced to endow Fuzzy ARTMAP with distributed learning. In addition, a quantitative study on a selected set of classification problems points out that problems have to present certain features in their output classes in order to noticeably reduce the number of recruited categories and achieve an acceptable classification accuracy. As part of this analysis, distributed learning was successfully adapted to a member of the Fuzzy ARTMAP family, FasArt, and similar procedures can be used to extend distributed learning capabilities to other Fuzzy ARTMAP based systems.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Parrado-Hernández</LastName><ForeName>Emilio</ForeName><Initials>E</Initials><AffiliationInfo><Affiliation>Departamento de Teoría de la Señal y Comunicaciones e Ingeniería Telemática, Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad de Valladolid, Camino del Cementerio S/N, 47011 Valladolid, Spain. emipar@tsc.uc3m.es</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Gómez-Sánchez</LastName><ForeName>Eduardo</ForeName><Initials>E</Initials></Author><Author ValidYN="Y"><LastName>Dimitriadis</LastName><ForeName>Yannis A</ForeName><Initials>YA</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Neural Netw</MedlineTA><NlmUniqueID>8805018</NlmUniqueID><ISSNLinking>0893-6080</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D017143">Fuzzy Logic</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D007858">Learning</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000502">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D016571">Neural Networks (Computer)</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D012984">Software</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="pubmed"><Year>2003</Year><Month>12</Month><Day>25</Day><Hour>5</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2004</Year><Month>2</Month><Day>11</Day><Hour>5</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2003</Year><Month>12</Month><Day>25</Day><Hour>5</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">14692638</ArticleId><ArticleId IdType="pii">S0893-6080(03)00009-1</ArticleId><ArticleId IdType="doi">10.1016/S0893-6080(03)00009-1</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Espagne</li></country><region><li>Castille-et-León</li></region></list><tree><noCountry><name sortKey="Dimitriadis, Yannis A" sort="Dimitriadis, Yannis A" uniqKey="Dimitriadis Y" first="Yannis A" last="Dimitriadis">Yannis A. Dimitriadis</name><name sortKey="G Mez Sanchez, Eduardo" sort="G Mez Sanchez, Eduardo" uniqKey="G Mez Sanchez E" first="Eduardo" last="G Mez-Sánchez">Eduardo G Mez-Sánchez</name></noCountry><country name="Espagne"><region name="Castille-et-León"><name sortKey="Parrado Hernandez, Emilio" sort="Parrado Hernandez, Emilio" uniqKey="Parrado Hernandez E" first="Emilio" last="Parrado-Hernández">Emilio Parrado-Hernández</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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