Automatically assigned DDC number: 00632
Manually assigned DDC number: 00632
Number of references: 4
Title: Second Differentials in Arbitrary Feed-Forward Neural Networks
Author:
Subject: Fabrice Rossi Second Differentials in Arbitrary Feed-Forward Neural Networks
Description: We extend here a general mathematical model for feed-forward neural networks. Such a network is represented as a vectorial function f of two variables, x (the input of the network) and w (the weight vector). We have already shown that the differential of f can be computed with an extended back-propagation algorithm as well as with a direct method. In this paper, we show that the second differentials of f can also be computed with several different algorithms. Evaluating the theoretical complexities of these methods allow to choose the fastest algorithm for a particular architecture. This will allow us to handle arbitrary feed-forward neural network learning with the help of recent training and analysis techniques based on the Hessian matrix of the error.
Contributor: The Pennsylvania State University CiteSeer Archives
Publisher: unknown
Date: 1996-07-15
Pubyear: 1995
Format: ps
Identifier: http://citeseer.ist.psu.edu/147586.html
Source: http://a48.ufrmd.dauphine.fr/~rossi/ieee.icnn.96.ps.gz
Language: en
Relation:
Relation:
Relation:
Relation:
Rights: unrestricted
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