We introduce a new formal model in which a learning algorithm must combine a collection of potentially poor but statistically independent hypothesis functions in order to approxima...
In this paper we demonstrate how weighted majority voting with multiplicative weight updating can be applied to obtain robust algorithms for learning binary relations. We first pre...
The Vapnik-Chervonenkis (V-C) dimension is an important combinatorial tool in the analysis of learning problems in the PAC framework. For polynomial learnability, we seek upper bou...
We describe three applications in computational learning theory of techniques and ideas recently introduced in the study of parameterized computational complexity. (1) Using param...
Rodney G. Downey, Patricia A. Evans, Michael R. Fe...
We consider the problem of learning in multilayer feed-forward networks of linear threshold units. We show that the Vapnik-Chervonenkis dimension of the class of functions that ca...
We introducea new learningproblem: learninga graph by piecemeal search, in which the learner must return every so often to its starting point (for refueling, say). We present two l...