Classifier Learning from Noisy Data as Probabilistic Evidence Combination

Steven W. Norton, Haym Hirsh

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.


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