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Inductive Logic Programming
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Author :Shan-Hwei Nienhuys-Cheng Publisher :Springer Science & Business Media ISBN 13 :9783540629276 Total Pages :440 pages Book Rating :4.6/5 (292 download)
Book Synopsis Foundations of Inductive Logic Programming by : Shan-Hwei Nienhuys-Cheng
Download or read book Foundations of Inductive Logic Programming written by Shan-Hwei Nienhuys-Cheng and published by Springer Science & Business Media. This book was released on 1997-04-18 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Inductive Logic Programming by : Francesco Bergadano
Download or read book Inductive Logic Programming written by Francesco Bergadano and published by MIT Press. This book was released on 1996 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Probabilistic Inductive Logic Programming by : Luc De Raedt
Download or read book Probabilistic Inductive Logic Programming written by Luc De Raedt and published by Springer. This book was released on 2008-02-26 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Learning Language in Logic by : James Cussens
Download or read book Learning Language in Logic written by James Cussens and published by . This book was released on 2014-01-15 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Inductive Logic Programming by : Nada Lavrač
Download or read book Inductive Logic Programming written by Nada Lavrač and published by Ellis Horwood. This book was released on 1994 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Machine Learning Proceedings 1994 by : William W. Cohen
Download or read book Machine Learning Proceedings 1994 written by William W. Cohen and published by Morgan Kaufmann. This book was released on 2014-06-28 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning Proceedings 1994
Book Synopsis Relational Data Mining by : Saso Dzeroski
Download or read book Relational Data Mining written by Saso Dzeroski and published by Springer Science & Business Media. This book was released on 2001-08 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Simply Logical written by Peter Flach and published by Wiley. This book was released on 1994-04-07 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Inductive Logic Programming by : Saso Dzeroski
Download or read book Inductive Logic Programming written by Saso Dzeroski and published by Springer. This book was released on 1999-06-09 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Encyclopedia of Machine Learning by : Claude Sammut
Download or read book Encyclopedia of Machine Learning written by Claude Sammut and published by Springer Science & Business Media. This book was released on 2011-03-28 with total page 1061 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Logical and Relational Learning by : Luc De Raedt
Download or read book Logical and Relational Learning written by Luc De Raedt and published by Springer Science & Business Media. This book was released on 2008-09-27 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: This first textbook on multi-relational data mining and inductive logic programming provides a complete overview of the field. It is self-contained and easily accessible for graduate students and practitioners of data mining and machine learning.
Download or read book Markov Logic written by Pedro Dechter and published by Springer Nature. This book was released on 2022-05-31 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Machine Learning Proceedings 1995 by : Armand Prieditis
Download or read book Machine Learning Proceedings 1995 written by Armand Prieditis and published by Morgan Kaufmann. This book was released on 2014-06-28 with total page 606 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning Proceedings 1995
Book Synopsis Lectures on Inductive Logic by : Jon Williamson
Download or read book Lectures on Inductive Logic written by Jon Williamson and published by Oxford University Press. This book was released on 2017 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: Inductive logic is a theory of how one should reason in the face of uncertainty. It has applications to decision making and artificial intelligence, as well as to scientific problems.
Book Synopsis Foundations of Rule Learning by : Johannes Fürnkranz
Download or read book Foundations of Rule Learning written by Johannes Fürnkranz and published by Springer Science & Business Media. This book was released on 2012-11-06 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rules – the clearest, most explored and best understood form of knowledge representation – are particularly important for data mining, as they offer the best tradeoff between human and machine understandability. This book presents the fundamentals of rule learning as investigated in classical machine learning and modern data mining. It introduces a feature-based view, as a unifying framework for propositional and relational rule learning, thus bridging the gap between attribute-value learning and inductive logic programming, and providing complete coverage of most important elements of rule learning. The book can be used as a textbook for teaching machine learning, as well as a comprehensive reference to research in the field of inductive rule learning. As such, it targets students, researchers and developers of rule learning algorithms, presenting the fundamental rule learning concepts in sufficient breadth and depth to enable the reader to understand, develop and apply rule learning techniques to real-world data.
Book Synopsis A Compendium of Machine Learning: Symbolic machine learning by : Garry Briscoe
Download or read book A Compendium of Machine Learning: Symbolic machine learning written by Garry Briscoe and published by Intellect (UK). This book was released on 1996 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning is a relatively new branch of artificial intelligence. The field has undergone a significant period of growth in the 1990s, with many new areas of research and development being explored.
Book Synopsis Program Synthesis by : Sumit Gulwani
Download or read book Program Synthesis written by Sumit Gulwani and published by . This book was released on 2017-07-11 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: Program synthesis is the task of automatically finding a program in the underlying programming language that satisfies the user intent expressed in the form of some specification. Since the inception of artificial intelligence in the 1950s, this problem has been considered the holy grail of Computer Science. Despite inherent challenges in the problem such as ambiguity of user intent and a typically enormous search space of programs, the field of program synthesis has developed many different techniques that enable program synthesis in different real-life application domains. It is now used successfully in software engineering, biological discovery, compute-raided education, end-user programming, and data cleaning. In the last decade, several applications of synthesis in the field of programming by examples have been deployed in mass-market industrial products. This monograph is a general overview of the state-of-the-art approaches to program synthesis, its applications, and subfields. It discusses the general principles common to all modern synthesis approaches such as syntactic bias, oracle-guided inductive search, and optimization techniques. We then present a literature review covering the four most common state-of-the-art techniques in program synthesis: enumerative search, constraint solving, stochastic search, and deduction-based programming by examples. It concludes with a brief list of future horizons for the field.
