Multi‐paradigm learning of declarative models

Article type: Other

Authors: Ferri, Cèsar

Affiliations: Dep. de Sistemes Informàtics i Computació, Universitat Politècnica de València, Cami de Vera, s/n, E‐46022 València, Spain E‐mail: cferri@dsic.upv.es

Abstract: This paper abstracts the contents of the PhD dissertation which has been recently defended by the author. Machine learning is the area of computer science that is concerned with the question of how to construct computer programs that automatically improve with experience. Recently, there have been important advances in the theoretical foundations of this field. At the same time, many successful applications have been developed: systems for extracting information from databases (data mining), applications to support decisions in medicine, telephone fraud and network intrusion detection, prediction of natural disasters, email filtering, document classification, and many others. This thesis introduces novel supervised learning methods that produce accurate and comprehensible models from past experiences which minimise the costs of generation and the costs of application.

Keywords: Machine learning, decision trees, inductive functional logic programming, cost‐sensitive learning

Journal: AI Communications, vol. 17, no. 2, pp. 95-97, 2004