Logic for learning

Learning comprehensible theories from structured data

J.W. Lloyd

266 pages, parution le 14/08/2003

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Résumé

This book is concerned with the rich and fruitful interplay between the fields of computational logic and machine learning. The intended audience is senior undergraduates, graduate students, and researchers in either of those fields. For those in computational logic, no previous knowledge of machine learning is assumed, and for those in machine learning no previous knowledge of computational logic is assumed.The logic used throughout the book is a higher-order one, since higher-order functions can have other functions as arguments and this capability can be exploited to provide abstractions for knowledge representation, methods for constructing predicates, and a foundation for logic-based computation.

The book should be of interest to researchers in machine learning, especially those who study learning methods for structured data. Throughout, great emphasis is placed on learning comprehensible theories. The book serves as an introduction for computational logicians to machine learning, a particularly interesting and important application area of logic, and also provides a foundation for functional logic programming languages.

Introduction
Logic
Individuals
Predicates
Computation
Learning
Appendix

References
Notation
Index

L'auteur - J.W. Lloyd

Australian National University, Canberra, ACT, Australia.

Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	J.W. Lloyd
Parution	14/08/2003
Nb. de pages	266
Format	16 x 24
Couverture	Relié
Poids	520g
Intérieur	Noir et Blanc
EAN13	9783540420279