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 study the average number of well-chosen labeled examples that are required for a helpful teacher to uniquely specify a target function within a concept class. This "average...
We introduce a framework, which we call Divide-by-2 (DB2), for extending support vector machines (SVM) to multi-class problems. DB2 offers an alternative to the standard one-again...
Abstract. We generalize a learning algorithm originally devised for deterministic all-accepting weighted tree automata (wta) to the setting of arbitrary deterministic wta. The lear...
Abstract. Most of the work in Machine Learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem...