[ ILPnet2 | Library | Newsletter | CSCW | Education | End-User Club | Events | Nodes | Systems | Applications | Members only ]
The use of functional and logic languages in machine learning
Peter A. Flach.
In Maria Alpuente, editor, Ninth International Workshop on Functional and
Logic Programming (WFLP2000), pages 225--237. Universidad Politecnica de
Valencia, September 2000. More behind this link..
Invited talk
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 Escher. The paper concentrates on giving a leisurely introduction to
ILP.
BibTeX entry.
Other publications
P A Flach,
Peter.Flach@bristol.ac.uk. Last modified on Wednesday 9 April 2003 at 18:31. © 2003 ILPnet2