Inductive Logic Programming: From Machine Learning to Software Engineering
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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
Format:Hardcover
Language:English
ISBN:0262023938
ISBN13:9780262023931
Release Date:December 1995
Publisher:MIT Press
Length:135 Pages
Weight:1.33 lbs.
Dimensions:0.8" x 7.3" x 9.3"
Age Range:18 years and up
Grade Range:Postsecondary and higher
Related Subjects
Artificial Intelligence Computer Science Computers Computers & Technology Education & Reference Human Vision & Language Systems Introductory & Beginning Languages & Tools Machine Learning Mathematics Object-Oriented Design Object-Oriented Software Design Programming Programming Languages Science & Math Software Software Design, Testing & EngineeringCustomer Reviews
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