A new learning method for single layer neural networks based on a regularized cost function

Authors:
Juan A. Suárez-Romero;Oscar Fontenla-Romero;Bertha Guijarro-Berdiñas;Amparo Alonso-Betanzos
Affiliations:
Department of Computer Science, University of A Coruña, A Coruña, Spain;Department of Computer Science, University of A Coruña, A Coruña, Spain;Department of Computer Science, University of A Coruña, A Coruña, Spain;Department of Computer Science, University of A Coruña, A Coruña, Spain
Venue:
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Year:
2003

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Abstract

In this paper a family of rule learners whose application is carried out according to a partial-matching criterion based on different purity measures is presented. The behavior of these rule learners is tested by solving a Text Categorisation problem. To illustrate the advantages of each learner, the MDL-based method of C4-5 is replaced by a pruning process whose performance relies on an estimation of the quality of the rules. Empirical results show that, in general, inducing partial-matching rules yields more compact rule sets without degrading performance measured in terms of microaveraged F1 which is one of the most common performance measure in Information Retrieval tasks. The experiments show that there are some purity measures which produces a number of rules significantly lesser than C4-5 meanwhile the performance measured with F1 is not degraded.