An empirical risk functional to improve learning in a neuro-fuzzy classifier.
The paper proposes a new Empirical Risk Functional as cost function for training neuro-fuzzy classifiers. This cost function, called Approximate Differentiable Empirical Risk Functional (ADERF), provides a differentiable approximation of the misclassification rate so that the Empirical Risk Minimization Principle formulated in Vapnik's Statistical Learning Theory can be applied. Also, based on the proposed ADERF, a learning algorithm is formulated. Experimental results on a number of benchmark classification tasks are provided and comparison to alternative approaches given.
Castellano
Giovanna
G
Department of Informatics, University of Bari, Bari, Italy. castellano@di.uniba.it
Fanelli
Anna M
AM
Mencar
Corrado
C