Please use this identifier to cite or link to this item:
|Title:||Estimating the number of hidden neurons in a feedforward network using the singular value decomposition|
|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)|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Sep 23, 2018
checked on Jun 8, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.