Learning with first, second, and no derivatives: A case study in high energy physics-Reference-Cited by-同舟云学术

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Learning with first, second, and no derivatives: A case study in high energy physics

Published:1994-04 Issue:2 Volume:6 Page:181-206
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Battiti Roberto,Tecchiolli Giampietro

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference33 articles.

1. Neural theory of association and concept-formation;Amari;Biol. Cybernet.,1977

2. A neural-net training program based on conjugate-gradient optimization;Barnard,1989

3. First- and second-order methods for learning: Between steepest descent and Newton's method;Battiti;Neural Computat.,1992

4. Accelerated back-propagation learning: Two optimization methods;Battiti;Complex Syst.,1989

5. BFGS optimization for faster and automated supervised learning;Battiti,1990

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