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ESANN
2008

A Regularized Learning Method for Neural Networks Based on Sensitivity Analysis

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A Regularized Learning Method for Neural Networks Based on Sensitivity Analysis
The Sensitivity-Based Linear Learning Method (SBLLM) is a learning method for two-layer feedforward neural networks, based on sensitivity analysis, that calculates the weights by solving a system of linear equations. Therefore, there is an important saving in computational time which significantly enhances the behavior of this method compared to other learning algorithms. This paper introduces a generalization of the SBLLM by adding a regularization term in the cost function. The theoretical basis for the method is given and its performance is illustrated.
Bertha Guijarro-Berdiñas, Oscar Fontenla-Ro
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where ESANN
Authors Bertha Guijarro-Berdiñas, Oscar Fontenla-Romero, Beatriz Pérez-Sánchez, Amparo Alonso-Betanzos
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