Abstract. This paper presents an approach for reasoning about action and change which appeals to the principle of Occam’s razor— roughly stating that the simplest explanations ...
We propose a formulation of the Decision Tree learning algorithm in the Compression settings and derive tight generalization error bounds. In particular, we propose Sample Compres...
Abstract. We establish a generic theoretical tool to construct probabilistic bounds for algorithms where the output is a subset of objects from an initial pool of candidates (or mo...