The use of functional and logic languages in machine learning

Peter Flach, Alpuente Maria

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

Traditionally, machine learning algorithms such as decision tree learners have employed attribute-value representations. From the early 80's on people have started to explore Prolog as a representation formalism for machine learning, an area which came to be called inductive logic programming (ILP). With hindsight, however, Prolog may not have been the best choice, since it can be argued that types and functions, well known from functional programming, are essential ingredients of the individual-centred representations employed in machine learning. Consequently, a combined functional logic language is a better vehicle for learning with a rich representation. In this talk I will illustrate this by means of the higher-order functional logic programming language \emphEscher. The paper concentrates on giving a leisurely introduction to ILP.
Translated title of the contributionThe use of functional and logic languages in machine learning
Original languageEnglish
Title of host publicationNinth International Workshop on Functional and Logic Programming (WFLP2000)
PublisherUniversidad Politecnica de Valencia.
Pages225 - 237
Number of pages12
Publication statusPublished - 2000

Bibliographical note

Other page information: 225-237
Conference Proceedings/Title of Journal: Ninth International Workshop on Functional and Logic Programming (WFLP2000)
Other identifier: 1000519

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