A NEW HYBRID EXPANSION FUNCTION BASED MUTUAL INFORMATION FOR A MULTILAYER NEURAL NETWORKS OPTIMIZATION
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Date
2017
Journal Title
Journal ISSN
Volume Title
Publisher
Oum-El-Bouaghi University
Abstract
Function expansion was used to expand initial features based on a non
linear transformation. Many known expansion functions are found such
the trigonometric, the polynomial, the Legendre polynomial, the power
series, the exponential and the logarithmic transformation. This paper
present a comparison between different expansion functions based on
mutual information and different performance functions. We propose a
new expansion process able to improve the correspondent mutual
information and the final performance. The process was tested; using
different benchmark databases, and shows his ability to improve results of
classification problems
Description
Keywords
Function expansion, Multilayer perceptron, Classification, Mutual information, Features selection