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Logic for Learning : Learning Comprehensible Theories from Structured Data - John W. Lloyd

Logic for Learning

Learning Comprehensible Theories from Structured Data

By: John W. Lloyd

Hardcover | 6 August 2003

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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.
Industry 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)

You Can Find This Book In

Non-FictionMathematicsMathematical FoundationMathematical LogicComputing & I.T.Computer ScienceMathematical Theory of ComputationComputer Architecture & Logic DesignArtificial Intelligence

Other Editions and Formats

Paperback

Published: 22nd October 2010

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