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A hybrid ART-GRNN online learning neural network with a ?-insensitive loss function

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dc.citedby40
dc.contributor.authorYap K.S.en_US
dc.contributor.authorLim C.P.en_US
dc.contributor.authorAbidin I.Z.en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid55666579300en_US
dc.contributor.authorid35606640500en_US
dc.date.accessioned2023-12-29T07:56:28Z
dc.date.available2023-12-29T07:56:28Z
dc.date.issued2008
dc.description.abstractIn this brief, a new neural network model called generalized adaptive resonance theory (GART) is introduced. GART is a hybrid model that comprises a modified Gaussian adaptive resonance theory (MGA) and the generalized regression neural network (GRNN). It is an enhanced version of the GRNN, which preserves the online learning properties of adaptive resonance theory (ART). A series of empirical studies to assess the effectiveness of GART in classification, regression, and time series prediction tasks is conducted. The results demonstrate that GART is able to produce good performances as compared with those of other methods, including the online sequential extreme learning machine (OSELM) and sequential learning radial basis function (RBF) neural network models. � 2008 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/TNN.2008.2000992
dc.identifier.epage1646
dc.identifier.issue9
dc.identifier.scopus2-s2.0-52149111368
dc.identifier.spage1641
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-52149111368&doi=10.1109%2fTNN.2008.2000992&partnerID=40&md5=7f6585894e735f5e16eeedbcc15ed951
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/30951
dc.identifier.volume19
dc.pagecount5
dc.sourceScopus
dc.sourcetitleIEEE Transactions on Neural Networks
dc.subjectAdaptive resonance theory (ART)
dc.subjectBayesian theorem
dc.subjectGeneralized regression neural network (GRNN)
dc.subjectOnline sequential extreme learning machine
dc.subjectAlgorithms
dc.subjectArtificial Intelligence
dc.subjectComputer Simulation
dc.subjectModels, Theoretical
dc.subjectNeural Networks (Computer)
dc.subjectOnline Systems
dc.subjectPattern Recognition, Automated
dc.subjectArts computing
dc.subjectE-learning
dc.subjectEducation
dc.subjectFeedforward neural networks
dc.subjectFood processing
dc.subjectImage classification
dc.subjectInternet
dc.subjectLearning systems
dc.subjectRadial basis function networks
dc.subjectResonance
dc.subjectTime series analysis
dc.subjectalgorithm
dc.subjectarticle
dc.subjectartificial intelligence
dc.subjectartificial neural network
dc.subjectautomated pattern recognition
dc.subjectcomputer simulation
dc.subjectmethodology
dc.subjectonline system
dc.subjecttheoretical model
dc.subjectAdaptive resonance theories
dc.subjectAdaptive resonance theory
dc.subjectAdaptive resonance theory (ART)
dc.subjectBayesian theorem
dc.subjectEmpirical studies
dc.subjectExtreme Learning Machine
dc.subjectGaussian
dc.subjectGeneralized regression neural network
dc.subjectGeneralized regression neural network (GRNN)
dc.subjectHybrid model
dc.subjectLoss functions
dc.subjectNeural network modelling
dc.subjectOn-line learning
dc.subjectOnline sequential extreme learning machine
dc.subjectRadial basis function neural networks
dc.subjectSequential learning
dc.subjectTime-series prediction
dc.subjectNeural networks
dc.titleA hybrid ART-GRNN online learning neural network with a ?-insensitive loss functionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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