Please use this identifier to cite or link to this item: https://doi.org/10.1007/11759966_126
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dc.titleEstimating the number of hidden neurons in a feedforward network using the singular value decomposition
dc.contributor.authorTeoh, E.J.
dc.contributor.authorXiang, C.
dc.contributor.authorTan, K.C.
dc.date.accessioned2014-04-24T08:34:59Z
dc.date.available2014-04-24T08:34:59Z
dc.date.issued2006
dc.identifier.citationTeoh, E.J., Xiang, C., Tan, K.C. (2006). Estimating the number of hidden neurons in a feedforward network using the singular value decomposition. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3971 LNCS : 858-865. ScholarBank@NUS Repository. https://doi.org/10.1007/11759966_126
dc.identifier.isbn354034439X
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/51159
dc.description.abstractWe attempt to quantify the significance of increasing the number of neurons in the hidden layer of a feedforward neural network architecture using the singular value decomposition (SVD). Through this, we extend some well-known properties of the SVD in evaluating the generalizability of single hidden layer feedforward networks (SLFNs) with respect to the number of hidden neurons. The generalization capability of the SLFN is measured by the degree of linear independency of the patterns in hidden layer space, which can be indirectly quantified from the singular values obtained from the SVD, in a post-learning step. © Springer-Verlag Berlin Heidelberg 2006.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/11759966_126
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1007/11759966_126
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.page858-865
dc.identifier.isiutNOT_IN_WOS
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