Logic for Learning : Learning Comprehensible Theories from Structured Data

John W. Lloyd

ISBN 10: 3540420274 / ISBN 13: 9783540420279

Edité par Springer, Springer Vieweg, 2003

A propos de cet article

Druck auf Anfrage Neuware - Printed after ordering - 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. Higher-order logic is already heavily used in some parts of computer science, for example, theoretical computer science, functional programming, and hardware verifica tion, mainly because of its great expressive power. Similar motivations apply here as well: 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, espe cially those who study learning methods for structured data. Machine learn ing applications are becoming increasingly concerned with applications for which the individuals that are the subject of learning have complex struc ture. Typical applications include text learning for the World Wide Web and bioinformatics. Traditional methods for such applications usually involve the extraction of features to reduce the problem to one of attribute-value learning.

N° de réf. du vendeur 9783540420279

Détails bibliographiques

• Titre: Logic for Learning : Learning Comprehensible Theories from Structured Data
• Éditeur: Springer, Springer Vieweg
• Date de parution: 2003
• Langue: anglais
• ISBN à 10 chiffres: 3540420274
• ISBN à 13 chiffres: 9783540420279
• Reliure: Buch
• État de l'article: Neu
• Dimensions: 241 L x 160 H x 20 P (en millimètres) 241 millimètres de largeur par 160 millimètres de hauteur par 20 millimètres de profondeur
• Poids: 576 grammes

A propos de ce titre

Synopsis

This book provides an approach to knowledge representation, computation, and learning using higher-order logic. It is aimed at researchers, graduate students, and senior undergraduates working in computational logic and/or machine learning.