PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Mutual information and gamma test for input selection
Nima Reyhani, Jin Hao, Yongnan Ji and Amaury Lendasse
In: ESANN 2005, European Symposium on Artificial Neural Networks, 27-29 April 2005, Bruges, Belgium.

Abstract

In this paper, input selection is performed using two different approaches. The first approach is based on the Gamma test. This test estimates the mean square error (MSE) that can be achieved without overfitting. The best set of inputs is the one that minimises the result of the Gamma test. The second method estimates the Mutual Information between a set of inputs and the output. The best set of inputs is the one that maximises the Mutual Information. Both methods are applied for the selection of the inputs for function approximation and time series prediction problems.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1683
Deposited By:Amaury Lendasse
Deposited On:28 November 2005