We identify four types of errors that unsupervised induction systems make and study each one in turn. Our contributions include (1) using a meta-model to analyze the incorrect bia...
Abstract. We describe EDRL-MD, an evolutionary algorithm-based system, for learning decision rules from databases. The main novelty of our approach lies in dealing with continuous ...
Abstract. In the paper, a new method of decision tree learning for costsensitive classification is presented. In contrast to the traditional greedy top-down inducer in the proposed...
Homologousproteins do not necessarily exhibit identical biochemicalfunction. Despitethis fact, local or global sequence similarity is widely used as an indication of functional id...
This paper describes a technique for learning both the number of states and the topologyof Hidden Markov Models from examples. The inductionprocess starts with the most specific m...