Logic for Learning

Learning Comprehensible Theories from Structured Data

  • J. W. Lloyd

Part of the Cognitive Technologies book series (COGTECH)

Table of contents

  1. Front Matter
    Pages I-X
  2. J. W. Lloyd
    Pages 1-29
  3. J. W. Lloyd
    Pages 31-82
  4. J. W. Lloyd
    Pages 83-130
  5. J. W. Lloyd
    Pages 131-181
  6. J. W. Lloyd
    Pages 183-206
  7. J. W. Lloyd
    Pages 207-241
  8. Back Matter
    Pages 243-259

About this book

Introduction

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.

Keywords

Prolog algorithmic learning artificial intelligence computational learning computational logic higher-order logic knowledge knowledge representation learning logic logic programming machine learning predicate construction

Authors and affiliations

  • J. W. Lloyd
    • 1
  1. 1.Research School of Information Sciences and Engineering, Computer Sciences LaboratoryThe Australian National UniversityCanberraAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-662-08406-9
  • Copyright Information Springer-Verlag Berlin Heidelberg 2003
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-642-07553-7
  • Online ISBN 978-3-662-08406-9
  • Series Print ISSN 1611-2482
  • About this book
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