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:

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Relation:

Rights: unrestricted

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