Overview
- The first book on learning and knowledge representation based on higher-order logic.
- Includes supplementary material: sn.pub/extras
Part of the book series: Cognitive Technologies (COGTECH)
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Table of contents (6 chapters)
Keywords
About this book
Reviews
From the reviews of the third edition:
"John has tried his hand at machine learning, and his aim in Logic for Learning is to demonstrate ‘the rich and fruitful interplay between the fields of computational logic and machine learning’. … As such, the book is more geared towards computational logicians who are interested in machine learning … . The book can also be used as a textbook in a mathematically oriented advanced graduate course. … it is indeed great stuff, which deserves to be taken serious by any computational logician … ." (Peter Flach, TLP – Theory and Practice of Logic Programming, Issue 4, 2004)
From the reviews:
"This book provides a systematic 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." (PHINEWS, Vol. 3, April, 2003)
Authors and Affiliations
Bibliographic Information
Book Title: Logic for Learning
Book Subtitle: Learning Comprehensible Theories from Structured Data
Authors: J. W. Lloyd
Series Title: Cognitive Technologies
DOI: https://doi.org/10.1007/978-3-662-08406-9
Publisher: Springer Berlin, Heidelberg
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eBook Packages: Springer Book Archive
Copyright Information: J. W. Lloyd 2003
Hardcover ISBN: 978-3-540-42027-9Published: 06 August 2003
Softcover ISBN: 978-3-642-07553-7Published: 22 October 2010
eBook ISBN: 978-3-662-08406-9Published: 17 April 2013
Series ISSN: 1611-2482
Series E-ISSN: 2197-6635
Edition Number: 1
Number of Pages: X, 257
Topics: Artificial Intelligence, Theory of Computation, Data Structures and Information Theory