The coverage of a learning algorithm is the number of concepts that can be learned by that algorithm from samples of a given size. This paper asks whether good learning algorithms can be designed by maximizing their coverage. The paper extends a previous upper bound on the coverage of any...
Although numerous Boolean concept learning algorithms have been introduced in the literature, little is known about what categories of concepts are actually learned satisfactorily by most of these algorithms. Conventional comparison studies, which test various algorithms in some chosen domain, do not provide such information, since their conclusions are limited...
This paper describes efficient methods for exact and approximate implementation of the MIN-FEATURES bias, which prefers consistent hypotheses definable over as few features as possible. This bias is useful for learning domains where many irrelevant features are present in the training data.
We first introduce FOCUS-2, a new algorithm that...