Classifier learning from noisy data as probabilistic evidence combination

Authors:
Steven W. Norton;Haym Hirsh
Affiliations:
Rutgers University, Department of Computer Science, New Brunswick, NJ and Siemens Corporate Research, Inc., Princeton, NJ;Rutgers University, Department of Computer Science, New Brunswick, NJ
Venue:
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Year:
1992

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Abstract

This paper presents an approach to learning from noisy data that views the problem as one of reasoning under uncertainty, where prior knowledge of the noise process is applied to compute a posteriori probabilities over the hypothesis space. In preliminary experiments this maximum a posteriori (MAP) approach exhibits a learning rate advantage over the C4.5 algorithm that is statistically significant.