Please use this identifier to cite or link to this item:
Type: Artigo de evento
Title: Projection Pursuit And The Solvability Condition Applied To Constructive Learning
Author: Von Zuben Fernando J.
de Andrade Netto Marcio L.
Abstract: Single hidden layer neural networks with supervised learning have been successfully applied to approximate unknown functions defined in compact functional spaces. The most advanced results also give rates of convergence, stipulating how many hidden neurons with a given activation function should be used to achieve a specific order of approximation. However, independently of the activation function employed, these connectionist models for function approximation suffer from a severe limitation: all hidden neurons use the same activation function. If each activation function of a hidden neuron is optimally defined for every approximation problem, then better rates of convergence will be achieved. This is exactly the purpose of constructive learning using projection pursuit techniques. Since the training process operates the hidden neurons individually, a pertinent activation function employing automatic smoothing splines can be iteratively developed for each neuron as a function of the learning set. Different from other papers, we apply projection pursuit in association with the optimization of the solvability condition, giving rise to a more efficient and accurate computational learning algorithm.
Editor: IEEE, Piscataway, NJ, United States
Rights: fechado
Identifier DOI: 
Date Issue: 1997
Appears in Collections:Unicamp - Artigos e Outros Documentos

Files in This Item:
File Description SizeFormat 
2-s2.0-0030650655.pdf550.92 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.