An empirical risk functional to improve learning in a neuro-fuzzy classifier

被引：26

作者：

Castellano, G ^{[1
]}

Fanelli, AM ^{[1
]}

Mencar, C ^{[1
]}

机构：

[1] Univ Bari, Dept Informat, Bari, Italy

来源：

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS | 2004年 / 34卷 / 01期

关键词：

classification error; empirical risk functional; gradient-based learning; misclassification rate; neuro-fuzzy classifier;

D O I：

10.1109/TSMCB.2003.811291

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：725 / 731

页数：7