title: An empirical risk functional to improve learning in a neuro-fuzzy classifier.
abstract: 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.
authors.affiliation: Department of Informatics, University of Bari, Bari, Italy. castellano@di.uniba.it
authors.forename: Giovanna
authors.initials: G
authors.lastname: Castellano
authors.forename: Anna M
authors.initials: AM
authors.lastname: Fanelli
authors.forename: Corrado
authors.initials: C
authors.lastname: Mencar