Automatic Regularization of Multilayered-Perceptron Training by Weight Orthogonalization

Balasundram P. Amavasai,; P.I. Rockett,

doi:10.1515/JISYS.2008.17.S1.57

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Automatic Regularization of Multilayered-Perceptron Training by Weight Orthogonalization

Balasundram P. Amavasai, and P.I. Rockett,

Published/Copyright: December 1, 2008

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From the journal Journal of Intelligent Systems Volume 17 Issue Supplement

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Amavasai,, Balasundram P. and Rockett,, P.I.. "Automatic Regularization of Multilayered-Perceptron Training by Weight Orthogonalization" Journal of Intelligent Systems, vol. 17, no. Supplement, 2008, pp. 57-86. https://doi.org/10.1515/JISYS.2008.17.S1.57

Amavasai,, B. & Rockett,, P. (2008). Automatic Regularization of Multilayered-Perceptron Training by Weight Orthogonalization. Journal of Intelligent Systems, 17(Supplement), 57-86. https://doi.org/10.1515/JISYS.2008.17.S1.57

Amavasai,, B. and Rockett,, P. (2008) Automatic Regularization of Multilayered-Perceptron Training by Weight Orthogonalization. Journal of Intelligent Systems, Vol. 17 (Issue Supplement), pp. 57-86. https://doi.org/10.1515/JISYS.2008.17.S1.57

Amavasai,, Balasundram P. and Rockett,, P.I.. "Automatic Regularization of Multilayered-Perceptron Training by Weight Orthogonalization" Journal of Intelligent Systems 17, no. Supplement (2008): 57-86. https://doi.org/10.1515/JISYS.2008.17.S1.57

Amavasai, B, Rockett, P. Automatic Regularization of Multilayered-Perceptron Training by Weight Orthogonalization. Journal of Intelligent Systems. 2008;17(Supplement): 57-86. https://doi.org/10.1515/JISYS.2008.17.S1.57

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Published Online: 2008-12

Articles in the same Issue

CONTENTS
Editorial: Cybernetic systems: Fuzzy, Neural and Evolutionary Computing Approaches
Nature Inspired Neural Network Ensemble Learning
Cybernetic Approach to Modeling Greenhouse Dynamics
Automatic Regularization of Multilayered-Perceptron Training by Weight Orthogonalization
Genetic Algorithm-based Modeling and Optimization of Control Parameters of an Air Motor
Genetic Programming Control of an Articulated Robotic Manipulator
Genetic Algorithm Optimization and Control System Design of Flexible Structures
Particle Swarm Intelligence to Optimize the Learning of N-tuples
State Estimation Methods for Sound Environment System Based on a Fuzzy Probability Theory

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Articles in the same Issue

CONTENTS
Editorial: Cybernetic systems: Fuzzy, Neural and Evolutionary Computing Approaches
Nature Inspired Neural Network Ensemble Learning
Cybernetic Approach to Modeling Greenhouse Dynamics
Automatic Regularization of Multilayered-Perceptron Training by Weight Orthogonalization
Genetic Algorithm-based Modeling and Optimization of Control Parameters of an Air Motor
Genetic Programming Control of an Articulated Robotic Manipulator
Genetic Algorithm Optimization and Control System Design of Flexible Structures
Particle Swarm Intelligence to Optimize the Learning of N-tuples
State Estimation Methods for Sound Environment System Based on a Fuzzy Probability Theory