Please use this identifier to cite or link to this item: https://doi.org/10.1007/11759966_79
Title: A fast learning algorithm based on layered hessian approximations and the pseudoinverse
Authors: Teoh, E.J.
Xiang, C. 
Tan, K.C. 
Issue Date: 2006
Source: Teoh, 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
Abstract: In 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.
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/10635/51076
ISBN: 354034439X
ISSN: 03029743
DOI: 10.1007/11759966_79
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