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Conference Paper: Wavelet network for nonlinear regression using probabilistic framework

TitleWavelet network for nonlinear regression using probabilistic framework
Authors
Issue Date2004
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
The International Symposium on Neural Networks, Dalian, China, 19-21 August 2004. In Lecture Notes in Computer Science, 2004, v. 3174, p. 731-736 How to Cite?
AbstractRegression analysis is an essential tools in most research fields such as signal processing, economic forecasting etc. In this paper, an regression algorithm using probabilistic wavelet network is proposed. As in most neural network (NN) regression methods, the proposed method can model nonlinear functions. Unlike other NN approaches, the proposed method is much robust to noisy data and thus over-fitting may not occur easily. This is because the use of wavelet representation in the hidden nodes and the probabilistic inference on the value of weights such that the assumption of smooth curve can be encoded implicitly. Experimental results show that the proposed network have higher modeling and prediction power than other common NN regression methods. © Springer-Verlag 2004.
Persistent Identifierhttp://hdl.handle.net/10722/137133
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252
References

 

DC FieldValueLanguage
dc.contributor.authorWong, SFen_HK
dc.contributor.authorWong, KYKen_HK
dc.date.accessioned2011-08-23T06:18:53Z-
dc.date.available2011-08-23T06:18:53Z-
dc.date.issued2004en_HK
dc.identifier.citationThe International Symposium on Neural Networks, Dalian, China, 19-21 August 2004. In Lecture Notes in Computer Science, 2004, v. 3174, p. 731-736en_HK
dc.identifier.issn0302-9743en_HK
dc.identifier.urihttp://hdl.handle.net/10722/137133-
dc.description.abstractRegression analysis is an essential tools in most research fields such as signal processing, economic forecasting etc. In this paper, an regression algorithm using probabilistic wavelet network is proposed. As in most neural network (NN) regression methods, the proposed method can model nonlinear functions. Unlike other NN approaches, the proposed method is much robust to noisy data and thus over-fitting may not occur easily. This is because the use of wavelet representation in the hidden nodes and the probabilistic inference on the value of weights such that the assumption of smooth curve can be encoded implicitly. Experimental results show that the proposed network have higher modeling and prediction power than other common NN regression methods. © Springer-Verlag 2004.en_HK
dc.languageeng-
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/en_HK
dc.relation.ispartofLecture Notes in Computer Scienceen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleWavelet network for nonlinear regression using probabilistic frameworken_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0302-9743&volume=3174&spage=731&epage=736&date=2004&atitle=Wavelet+network+for+nonlinear+regression+using+probabilistic+framework-
dc.identifier.emailWong, KYK:kykwong@cs.hku.hken_HK
dc.identifier.authorityWong, KYK=rp01393en_HK
dc.description.naturepostprint-
dc.identifier.scopuseid_2-s2.0-35048831301en_HK
dc.identifier.hkuros96722-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-35048831301&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume3174en_HK
dc.identifier.spage731en_HK
dc.identifier.epage736en_HK
dc.publisher.placeGermanyen_HK
dc.description.otherThe International Symposium on Neural Networks, Dalian, China, 19-21 August 2004. In Lecture Notes in Computer Science, 2004, v. 3174, p. 731-736-
dc.identifier.scopusauthoridWong, SF=22236051500en_HK
dc.identifier.scopusauthoridWong, KYK=24402187900en_HK

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