Meta-learning is an efficient approach in the field of machine learning, which involves multiple classifiers. In this paper, a meta-learning framework consisting of stacking meta-...
We describe a method for enriching the output of a parser with information available in a corpus. The method is based on graph rewriting using memorybased learning, applied to dep...
This paper describes the participation of the School of Informatics, University of Wales, Bangor in the 2004 Text Retrieval Conference. We present additions and modications to the...
This paper presents snlp+ebl, the first implementation of explanation based learning techniques for a partial order planner. We describe the basic learning framework of snlp+ebl, ...
Using Shafer and Vovk's game-theoretic framework for probability, we derive a capital asset pricing model from an efficient market hypothesis, with no assumptions about the b...