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Type: Artigo de evento
Title: Unit-growing Learning Optimizing The Solvability Condition For Model-free Regression
Author: Von Zuben Fernando J.
de Andrade Netto Marcio L.
Abstract: The universal approximation capability exhibited by one-hidden layer neural networks is explored to produce a supervised unit-growing learning for model-free nonlinear regression. The development is based on the solvability condition, which attests that the ability to learn a specific learning set increases with the number of nodes in the hidden layer. Since the training process operates the hidden nodes individually, a pertinent activation function can be iteratively developed for each node as a function of the learning set. The optimization of the solvability condition gives rise to neural networks of minimum dimension, an important step toward improving generalization.
Editor: IEEE, Piscataway, NJ, United States
Rights: fechado
Identifier DOI: 
Date Issue: 1995
Appears in Collections:Unicamp - Artigos e Outros Documentos

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