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Learning with maximum-entropy distributions
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DOI：10.1023/A:1010950718922.png)
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Abstract
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We are interested in distributions which are derived as a maximum entropy distribution from a given set of constraints. More specifically, we are interested in the case where the constraints are the expectation of individual and pairs of attributes. For such a given maximum entropy distribution (with some technical restrictions) we develop an efficient learning algorithm for read-once DNF. We extend our results to monotone read-k DNF following the techniques of (Hancock & Mansour, 1991).
Keywords:
PAC-learning
maximum entropy
learning algorithms
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