Author: Francesco Bergadano
Publisher: MIT Press
ISBN: 9780262023931
Category : Computers
Languages : en
Pages : 264
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Book Description
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
Author: Shan-Hwei Nienhuys-Cheng
Publisher: Springer Science & Business Media
ISBN: 9783540629276
Category : Computers
Languages : en
Pages : 440
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Book Description
The state of the art of the bioengineering aspects of the morphology of microorganisms and their relationship to process performance are described in this volume. Materials and methods of the digital image analysis and mathematical modeling of hyphal elongation, branching and pellet formation as well as their application to various fungi and actinomycetes during the production of antibiotics and enzymes are presented.
Author: Luc De Raedt
Publisher: Springer
ISBN: 354078652X
Category : Computers
Languages : en
Pages : 348
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Book Description
This book provides an introduction to probabilistic inductive logic programming. It places emphasis on the methods based on logic programming principles and covers formalisms and systems, implementations and applications, as well as theory.
Author: Nada Lavrač
Publisher: Ellis Horwood
ISBN:
Category : Computers
Languages : en
Pages : 328
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Book Description
Author: William W. Cohen
Publisher: Morgan Kaufmann
ISBN: 1483298183
Category : Computers
Languages : en
Pages : 398
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Book Description
Machine Learning Proceedings 1994
Author: Saso Dzeroski
Publisher: Springer Science & Business Media
ISBN: 9783540422891
Category : Business & Economics
Languages : en
Pages : 422
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Book Description
As the first book devoted to relational data mining, this coherently written multi-author monograph provides a thorough introduction and systematic overview of the area. The first part introduces the reader to the basics and principles of classical knowledge discovery in databases and inductive logic programming; subsequent chapters by leading experts assess the techniques in relational data mining in a principled and comprehensive way; finally, three chapters deal with advanced applications in various fields and refer the reader to resources for relational data mining. This book will become a valuable source of reference for R&D professionals active in relational data mining. Students as well as IT professionals and ambitioned practitioners interested in learning about relational data mining will appreciate the book as a useful text and gentle introduction to this exciting new field.
Author: Peter Flach
Publisher: Wiley
ISBN: 9780471942153
Category : Computers
Languages : en
Pages : 256
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Book Description
An introduction to Prolog programming for artificial intelligence covering both basic and advanced AI material. A unique advantage to this work is the combination of AI, Prolog and Logic. Each technique is accompanied by a program implementing it. Seeks to simplify the basic concepts of logic programming. Contains exercises and authentic examples to help facilitate the understanding of difficult concepts.
Author: Saso Dzeroski
Publisher: Springer
ISBN: 9783540661092
Category : Computers
Languages : en
Pages : 312
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Book Description
This book constitutes the refereed proceedings of the 9th International Conference on Inductive Logic Programming, ILP-99, held in Bled, Slovenia, in June 1999. The 24 revised papers presented were carefully reviewed and selected from 40 submissions. Also included are abstracts of three invited contributions. The papers address all current issues in inductive logic programming and inductive learning, from foundational and methodological issues to applications, e.g. in natural language processing, knowledge discovery, and data mining.
Author: Claude Sammut
Publisher: Springer Science & Business Media
ISBN: 0387307680
Category : Computers
Languages : en
Pages : 1061
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Book Description
This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.
Author: Pedro Dechter
Publisher: Springer Nature
ISBN: 3031015495
Category : Computers
Languages : en
Pages : 145
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Book Description
Most subfields of computer science have an interface layer via which applications communicate with the infrastructure, and this is key to their success (e.g., the Internet in networking, the relational model in databases, etc.). So far this interface layer has been missing in AI. First-order logic and probabilistic graphical models each have some of the necessary features, but a viable interface layer requires combining both. Markov logic is a powerful new language that accomplishes this by attaching weights to first-order formulas and treating them as templates for features of Markov random fields. Most statistical models in wide use are special cases of Markov logic, and first-order logic is its infinite-weight limit. Inference algorithms for Markov logic combine ideas from satisfiability, Markov chain Monte Carlo, belief propagation, and resolution. Learning algorithms make use of conditional likelihood, convex optimization, and inductive logic programming. Markov logic has been successfully applied to problems in information extraction and integration, natural language processing, robot mapping, social networks, computational biology, and others, and is the basis of the open-source Alchemy system. Table of Contents: Introduction / Markov Logic / Inference / Learning / Extensions / Applications / Conclusion
