Improving the GRLVQ Algorithm by the Cross Entropy Method View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2007

AUTHORS

Abderrahmane Boubezoul , Sébastien Paris , Mustapha Ouladsine

ABSTRACT

This paper discusses an alternative approach to parameter optimization of prototype-based learning algorithms that aim to minimize an objective function based on gradient search. The proposed approach is a stochastic optimization method called the Cross Entropy (CE) method. The CE method is used to tackle the initialization sensitiveness problem associated with the original generalized Learning Vector Quantization (GLVQ) algorithm and its variants and to locate the globally optimal solutions. We will focus our study on a variant which deals with a weighted norm instead of the Euclidean norm in order to select the most relevant features. The results in this paper indicate that the CE method can successfully be applied to this kind of problems and efficiently generate high quality solutions. Also, highly competitive numerical results on real world data sets are reported. More... »

PAGES

199-208

Book

TITLE

Artificial Neural Networks – ICANN 2007

ISBN

978-3-540-74689-8
978-3-540-74690-4

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-74690-4_21

DOI

http://dx.doi.org/10.1007/978-3-540-74690-4_21

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1029775555


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