Structural adaptation of parsimonious higher-order neural classifiers-Reference-Cited by-同舟云学术

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Structural adaptation of parsimonious higher-order neural classifiers

Published:1994-01 Issue:2 Volume:7 Page:279-289
ISSN:0893-6080
Container-title:Neural Networks
language:en
Short-container-title:Neural Networks

Author:

Fahner Gerald,Eckmiller Rolf

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience

Reference26 articles.

1. The Vapnik-Chervonenkis dimension: Information versus complexity in learning;Abu Mostafa;Neural Computation,1989

2. Orthogonal transforms for digital signal processing;Ahmed,1975

3. Constructing hidden units using examples and queries;Baum,1991

4. Supervised learning of probability distributions by neural networks;Baum,1987

5. Learning in higher-order ‘artificial dendritic trees.’;Bell,1990

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1. Combined weight and density bounds on the polynomial threshold function representation of Boolean functions;Discrete Mathematics;2022-08

2. Sign-representation of Boolean functions using a small number of monomials;Neural Networks;2009-09

3. Variations of the two-spiral task;Connection Science;2007-06

4. On a perceptron-type learning rule for higher order Hopfield neural networks including dilation and translation;Neurocomputing;2002-10

5. EFFECTS OF DILATION AND TRANSLATION ON A PERCEPTRON-TYPE LEARNING RULE FOR HIGHER ORDER HOPFIELD NEURAL NETWORKS;International Journal of Neural Systems;2002-04

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