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A Truly Online Learning Algorithm using Hybrid Fuzzy ARTMAP and Online Extreme Learning Machine for Pattern Classification

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dc.citedby24
dc.contributor.authorWong S.Y.en_US
dc.contributor.authorYap K.S.en_US
dc.contributor.authorYap H.J.en_US
dc.contributor.authorTan S.C.en_US
dc.contributor.authorid55812054100en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid35319362200en_US
dc.contributor.authorid7403366395en_US
dc.date.accessioned2023-05-29T05:59:38Z
dc.date.available2023-05-29T05:59:38Z
dc.date.issued2015
dc.descriptionAlgorithms; Benchmarking; E-learning; Knowledge acquisition; Learning systems; Pattern recognition; Bench-mark problems; Efficient learning; Extreme learning machine; Fuzzy ARTMAP; Generalization performance; Online learning; Online learning algorithms; Online sequential extreme learning machine; Learning algorithmsen_US
dc.description.abstractThis paper presents a Hybrid Fuzzy ARTMAP (FAM) and Online Extreme learning machine (OELM), hereafter denoted as FAM-OELM, which enables online learning to start from the first trained data samples without having to set up an initialization phase which requires a chunk of data samples to be ready prior to training. The idea of developing FAM-OELM is motivated by the ELM concept proposed by Huang et al., for being an efficient learning algorithm that provides better generalization performance at a much faster learning speed. However, different from the batch learning ELM and its variant called the online sequential extreme learning machine which still requires an initial offline training phase before it can turn into online training, the proposed FAM-OELM showcases a framework that enable online learning to commence right from the first data sample. Here, classification can be conducted at any time during the training phase. Such appealing feature of the proposed algorithm has strictly fulfilled the criteria of being truly sequential, while many of the existing algorithms are not. In addition, FAM-OELM automatically grows hidden neuron such that the network can accommodate new information without over fitting and compromising on the knowledge learnt earlier. The simulation results reveal the efficacy and validity of FAM-OELM when it is applied to a real world application and various benchmark problems. � 2014, Springer Science+Business Media New York.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s11063-014-9374-5
dc.identifier.epage602
dc.identifier.issue3
dc.identifier.scopus2-s2.0-84952987391
dc.identifier.spage585
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84952987391&doi=10.1007%2fs11063-014-9374-5&partnerID=40&md5=48ca321cf7d1e15ea841b91f8c0b448e
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22209
dc.identifier.volume42
dc.publisherSpringer New York LLCen_US
dc.sourceScopus
dc.sourcetitleNeural Processing Letters
dc.titleA Truly Online Learning Algorithm using Hybrid Fuzzy ARTMAP and Online Extreme Learning Machine for Pattern Classificationen_US
dc.typeArticleen_US
dspace.entity.typePublication
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