A Global Optimum Approach for One-Layer Neural Networks
Abstract
The article presents a method for learning the weights in one-layer feed-forward neural networks minimizing either the sum of squared errors or the maximum absolute error, measured in the input scale. This leads to the existence of a global optimum that can be easily obtained solving linear systems of equations or linear programming problems, using much less computational power than the one associated with the standard methods. Another version...
Paper Details
Title
A Global Optimum Approach for One-Layer Neural Networks
Published Date
Jun 1, 2002
Journal
Volume
14
Issue
6
Pages
1429 - 1449
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