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Inductive logic programming : from machine learning to software engineering

Although Inductive Logic Programming (ILP) is generally thought of as a research area at the intersection of machine learning and computational logic, Bergadano and Gunetti propose that most of the research in ILP has in fact come from machine learning, particularly in the evolution of inductive reasoning from pattern recognition, through initial approaches to symbolic machine learning, to recent techniques for learning relational concepts. In this book they provide an extended, up-to-date survey of ILP, emphasizing methods and systems suitable for software engineering applications, including inductive program development, testing, and maintenance. Inductive Logic Programming includes a definition of the basic ILP problem and its variations (incremental, with queries, for multiple predicates and predicate invention capabilities), a description of bottom-up operators and techniques (such as least general generalization, inverse resolution, and inverse implication), an analysis of top-down methods (mainly MIS and FOIL-like systems), and a survey of methods and languages for specifying inductive bias Logic Programming series.OCLC-licensed vendor bibliographic record.

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Detail Information
Series Title
-
Call Number
005 BER i
Publisher
Cambridge, Mass. : : MIT Press,.,
Collation
1 online resource (vii, 240 pages) : illustrations.
Language
English
ISBN/ISSN
9780262288422
Classification
005
Content Type
text
Media Type
computer
Carrier Type
online resource
Edition
-
Subject(s)
Logic programming.
Specific Detail Info
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Statement of Responsibility
Other Information
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Source
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Validator
maya
Digital Object Identifier (DOI)
https://doi.org/10.7551/mitpress/3731.001.0001
Journal Volume
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Journal Issue
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Subtitle
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Parallel Title
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