DocumentCode
1161588
Title
Optimal convergence of on-line backpropagation
Author
Gori, Marco ; Maggini, Marco
Author_Institution
Dept. of Syst. & Inf., Firenze Univ., Italy
Volume
7
Issue
1
fYear
1996
fDate
1/1/1996 12:00:00 AM
Firstpage
251
Lastpage
254
Abstract
Many researchers are quite skeptical about the actual behavior of neural network learning algorithms like backpropagation. One of the major problems is with the lack of clear theoretical results on optimal convergence, particularly for pattern mode algorithms. In this paper, we prove the companion of Rosenblatt´s PC (perceptron convergence) theorem for feedforward networks (1960), stating that pattern mode backpropagation converges to an optimal solution for linearly separable patterns
Keywords
backpropagation; convergence; feedforward neural nets; optimisation; perceptrons; feedforward networks; linearly separable patterns; neural network learning algorithms; online backpropagation; optimal convergence; pattern mode backpropagation; perceptron convergence theorem; Backpropagation algorithms; Computer networks; Convergence; Cost function; Equations; Neural networks; Neurons; Pattern analysis; Shape;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/72.478415
Filename
478415
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