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|Title:||Estimating the number of hidden neurons in a feedforward network using the singular value decomposition||Authors:||Teoh, E.J.
|Issue Date:||2006||Citation:||Teoh, 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||Abstract:||We 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.||Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)||URI:||http://scholarbank.nus.edu.sg/handle/10635/51159||ISBN:||354034439X||ISSN:||03029743||DOI:||10.1007/11759966_126|
|Appears in Collections:||Staff Publications|
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