This paper considers the Valiant framework as it is applied to the task of learning logical concepts from random examples. It is argued that the current interpretation of this Val...
In this work, we study the problem of within-network relational learning and inference, where models are learned on a partially labeled relational dataset and then are applied to ...
We describe a new approach for understanding the difficulty of designing efficient learning algorithms. We prove that the existence of an efficient learning algorithm for a circui...
The naive Bayesian classifier provides a simple and effective approach to classifier learning, but its attribute independence assumption is often violated in the real world. A numb...
A team of learning machines is a multiset of learning machines. A team is said to successfully learn a concept just in case each member of some nonempty subset, of predetermined s...