Abstract. This paper proposes a generic extension to propositional rule learners to handle multiple-instance data. In a multiple-instance representation, each learning example is r...
In this paper, we investigate the role of a biomedical dataset on the classification accuracy of an algorithm. We quantify the complexity of a biomedical dataset using five complex...
Abstract. Traditionally, machine learning algorithms such as decision tree learners have employed attribute-value representations. From the early 80's on people have started t...
We propose in this paper a general framework for integrating inductive and case-based reasoning techniques for diagnosis tasks. We present a set of practical integrated approaches...
We present an approach for learning part-of-speech distinctions by induction over the lexicon of the Cyc knowledge base. This produces good results (74.6%) using a decision tree t...