Please use this identifier to cite or link to this item:
|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|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.