Logic for Learning
Learning Comprehensible Theories from Structured Data
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Beschreibung
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.
The first book on learning and knowledge representation based on higher-order logic. Includes supplementary material: sn.pub/extras
Autor*in
John W. Lloyd
Themen in »Logic for Learning«
Prolog algorithmic learning artificial intelligence computational learning computational logic higher-order logic knowledge knowledge representation learning logic logic programming machine learning predicate construction
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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)
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