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An empirical risk functional to improve learning in a neuro-fuzzy classifier
- Source :
- IEEE Transactions on Systems, Man, and Cybernetics--Part B: Cybernetics. Feb, 2004, Vol. 34 Issue 1, p725, 7 p.
- Publication Year :
- 2004
-
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. Index Terms--Classification error, empirical risk functional, gradient-based learning, misclassification rate, neuro-fuzzy classifier.
Details
- Language :
- English
- ISSN :
- 10834419
- Volume :
- 34
- Issue :
- 1
- Database :
- Gale General OneFile
- Journal :
- IEEE Transactions on Systems, Man, and Cybernetics--Part B: Cybernetics
- Publication Type :
- Academic Journal
- Accession number :
- edsgcl.113094204