PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Selection of important input variables for RBF networks using partial serivatives
Jarkko Tikka and Jaakko Hollmen
In: Proceedings of the 16th European Symposium on Neural Networks (ESANN 2008) (2008) d-side , Bruges, Belgium , pp. 167-172.

Abstract

In regression problems, making accurate predictions is often the primary goal. Also, relevance of inputs in the prediction of an output would be valuable information in many cases. A sequential input selection algorithm for Radial basis function (SISAL-RBF) networks is presented to analyze importances of the inputs. The ranking of inputs is based on values, which are evaluated from the partial derivatives of the network. The proposed method is applied to benchmark data sets. It yields accurate prediction models, which are parsimonious in terms of the input variables.

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4202
Deposited By:Jaakko Hollmen
Deposited On:21 November 2008