Automatically acquiring control-knowledge for planning, as it is the case for Machine Learning in general, strongly depends on the training examples. In the case of planning, examp...
We initiate the study of incentives in a general machine learning framework. We focus on a game-theoretic regression learning setting where private information is elicited from mu...
This paper addresses the problem of classifying observations when features are context-sensitive, specifically when the testing set involves a context that is different from the t...
Computational comparison is made between two feature selection approaches for nding a separating plane that discriminates between two point sets in an n-dimensional feature space ...
We explore algorithms for learning classification procedures that attempt to minimize the cost of misclassifying examples. First, we consider inductive learning of classification ...
Michael J. Pazzani, Christopher J. Merz, Patrick M...