Please use this identifier to cite or link to this item: https://doi.org/10.1007/11759966_79
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dc.titleA fast learning algorithm based on layered hessian approximations and the pseudoinverse
dc.contributor.authorTeoh, E.J.
dc.contributor.authorXiang, C.
dc.contributor.authorTan, K.C.
dc.date.accessioned2014-04-24T08:32:28Z
dc.date.available2014-04-24T08:32:28Z
dc.date.issued2006
dc.identifier.citationTeoh, E.J., Xiang, C., Tan, K.C. (2006). A fast learning algorithm based on layered hessian approximations and the pseudoinverse. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3971 LNCS : 530-536. ScholarBank@NUS Repository. https://doi.org/10.1007/11759966_79
dc.identifier.isbn354034439X
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/51076
dc.description.abstractIn this article, we present a simple, effective method to learning for an MLP that is based on approximating the Hessian using only local information, specifically, the correlations of output activations from previous layers of hidden neurons. This approach of training the hidden layer weights with the Hessian approximation combined with the training of the final output layer of weights using the pseudoinverse [1] yields improved performance at a fraction of the computational and structural complexity of conventional learning algorithms. © Springer-Verlag Berlin Heidelberg 2006.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/11759966_79
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1007/11759966_79
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume3971 LNCS
dc.description.page530-536
dc.identifier.isiutNOT_IN_WOS
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