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Hardcover | ISBN: 9780262023931 | 135 pp. | 5.9 x 8.8 in | December 1995
 

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Inductive Logic Programming

From Machine Learning to Software Engineering

Overview

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