The foundations of cost-sensitive learning

Authors:
Charles Elkan
Affiliations:
Department of Computer Science and Engineering, University of California, San Diego, California
Venue:
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Year:
2001

Citing 7
Cited 219

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Abstract

Extracting rules from RBFs is not a trivial task because of nonlinear functions or high input dimensionality. In such cases, some of the hidden units of the RBF network have a tendency to be "shared" across several output classes or even may not contribute ...