Machine learning from examples: Inductive and Lazy methods

Important approaches to inductive learning methods such as propositional and relational learners, with an emphasis in Inductive Logic Programming based methods, are reported, as well as to lazy methodssuch as instance-based and case-based reasoning.

Abstract

Machine Learning from examples may be used, within Artificial Intelligence, as a way to acquire general knowledge or associate to a concrete problem solving system. Inductive learning methods are typically used to acquire general knowledge from examples. Lazy methods are those in which the experience is accessed, selected and used in a problem-centered way. In this paper we report important approaches to inductive learning methods such as propositional and relational learners, with an emphasis in Inductive Logic Programming based methods, as well as to lazy methods such as instance-based and case-based reasoning.