PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Determination of the Mahalanobis matrix using nonparametric noise estimations
Amaury Lendasse, Francesco Corona, Jin Hao, Nima Reyhani and Michel Verleysen
In: ESANN 2006, European Symposium on Artificial Neural Networks, 26-28 April 2006, Bruges (Belgium),.

Abstract

In this paper, the problem of an optimal transformation of the input space for function approximation problems is ddressed. The transformation is defined determining the Mahalanobis matrix that minimizes the variance of noise. To compute variance of the noise, a nonparametric estimator called the Delta Test paradigm is used. The proposed approach is illlustrated on two different benchmarks.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:2579
Deposited By:Amaury Lendasse
Deposited On:22 November 2006