Some Results on Learning

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Abstract
This paper presents some formal results on learning. In particular, it concerns algorithms that learn sets and functions from examples. We seek conditions necessary and sufficient for learning over a range of probabilistic models for such algorithms. This paper concerns algorithms that learn sets and functions from examples for them. The motivation behind the study is a need to better understand the class of problems known as 'concept learning problems' in the Artificial Intelligence literature.

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