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Probabilistic Inductive Logic Programming

Probabilistic Inductive Logic Programming PDF Author: Luc De Raedt
Publisher: Springer Science & Business Media
ISBN: 3540786511
Category : Computers
Languages : en
Pages : 339

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Book Description
The question, how to combine probability and logic with learning, is getting an increased attention in several disciplines such as knowledge representation, reasoning about uncertainty, data mining, and machine learning simulateously. This results in the newly emerging subfield known under the names of statistical relational learning and probabilistic inductive logic programming. This book provides an introduction to the field with an emphasis on the methods based on logic programming principles. It is concerned with formalisms and systems, implementations and applications, as well as with the theory of probabilistic inductive logic programming. The 13 chapters of this state-of-the-art survey start with an introduction to probabilistic inductive logic programming; moreover the book presents a detailed overview of the most important probabilistic logic learning formalisms and systems such as relational sequence learning techniques, using kernels with logical representations, Markov logic, the PRISM system, CLP(BN), Bayesian logic programs, and the independent choice logic. The third part provides a detailed account of some show-case applications of probabilistic inductive logic programming. The final part touches upon some theoretical investigations and includes chapters on behavioural comparison of probabilistic logic programming representations and a model-theoretic expressivity analysis.

Inductive Logic Programming

Inductive Logic Programming PDF Author: Hendrik Blockeel
Publisher: Springer Science & Business Media
ISBN: 3540784683
Category : Computers
Languages : en
Pages : 306

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Book Description
This book constitutes the thoroughly refereed post-conference proceedings of the 17th International Conference on Inductive Logic Programming, ILP 2007, held in Corvallis, OR, USA, in June 2007 in conjunction with ICML 2007, the International Conference on Machine Learning. The 15 revised full papers and 11 revised short papers presented together with 2 invited lectures were carefully reviewed and selected from 38 initial submissions. The papers present original results on all aspects of learning in logic, as well as multi-relational learning and data mining, statistical relational learning, graph and tree mining, relational reinforcement learning, and learning in other non-propositional knowledge representation frameworks. Thus all current topics in inductive logic programming, ranging from theoretical and methodological issues to advanced applications in various areas are covered.

Inductive Logic Programming

Inductive Logic Programming PDF Author: Rui Camacho
Publisher: Springer Science & Business Media
ISBN: 3540229418
Category : Computers
Languages : en
Pages : 358

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Book Description
This book constitutes the refereed proceedings of the 14th International Conference on Inductive Logic Programming, ILP 2004, held in Porto, Portugal, in September 2004. The 20 revised full papers presented were carefully reviewed and selected for inclusion in the book. The papers address all current topics in inductive logic programming, ranging from theoretical and methodological issues to advanced applications in various areas.

Probabilistic Inductive Logic Programming

Probabilistic Inductive Logic Programming PDF Author: Luc De Raedt
Publisher: Springer Science & Business Media
ISBN: 3540786511
Category : Computers
Languages : en
Pages : 339

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Book Description
The question, how to combine probability and logic with learning, is getting an increased attention in several disciplines such as knowledge representation, reasoning about uncertainty, data mining, and machine learning simulateously. This results in the newly emerging subfield known under the names of statistical relational learning and probabilistic inductive logic programming. This book provides an introduction to the field with an emphasis on the methods based on logic programming principles. It is concerned with formalisms and systems, implementations and applications, as well as with the theory of probabilistic inductive logic programming. The 13 chapters of this state-of-the-art survey start with an introduction to probabilistic inductive logic programming; moreover the book presents a detailed overview of the most important probabilistic logic learning formalisms and systems such as relational sequence learning techniques, using kernels with logical representations, Markov logic, the PRISM system, CLP(BN), Bayesian logic programs, and the independent choice logic. The third part provides a detailed account of some show-case applications of probabilistic inductive logic programming. The final part touches upon some theoretical investigations and includes chapters on behavioural comparison of probabilistic logic programming representations and a model-theoretic expressivity analysis.

Latest Advances in Inductive Logic Programming

Latest Advances in Inductive Logic Programming PDF Author: Stephen H Muggleton
Publisher: World Scientific
ISBN: 1783265108
Category : Computers
Languages : en
Pages : 264

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Book Description
This book represents a selection of papers presented at the Inductive Logic Programming (ILP) workshop held at Cumberland Lodge, Great Windsor Park. The collection marks two decades since the first ILP workshop in 1991. During this period the area has developed into the main forum for work on logic-based machine learning. The chapters cover a wide variety of topics, ranging from theory and ILP implementations to state-of-the-art applications in real-world domains. The international contributors represent leaders in the field from prestigious institutions in Europe, North America and Asia. Graduate students and researchers in this field will find this book highly useful as it provides an up-to-date insight into the key sub-areas of implementation and theory of ILP. For academics and researchers in the field of artificial intelligence and natural sciences, the book demonstrates how ILP is being used in areas as diverse as the learning of game strategies, robotics, natural language understanding, query search, drug design and protein modelling. Contents:Applications:Can ILP Learn Complete and Correct Game Strategies? (Stephen H Muggleton and Changze Xu)Induction in Nonmonotonic Causal Theories for a Domestic Service Robot (Jianmin Ji and Xiaoping Chen)Using Ontologies in Semantic Data Mining with g-SEGS and Aleph (Anže Vavpetič and Nada Lavră)Improving Search Engine Query Expansion Techniques with ILP (José Carlos Almeida Santos and Manuel Fonseca de Sam Bento Ribeiro)ILP for Cosmetic Product Selection (Hiroyuki Nishiyama and Fumio Mizoguchi)Learning User Behaviours in Real Mobile Domains (Andreas Markitanis, Domenico Corapi, Alessandra Russo and Emil C Lupu)Discovering Ligands for TRP Ion Channels Using Formal Concept Analysis (Mahito Sugiyama, Kentaro Imajo, Keisuke Otaki and Akihiro Yamamoto)Predictive Learning in Two-Way Datasets (Beau Piccart, Hendrik Blockeel, Andy Georges and Lieven Eeckhout)Model of Double-Strand Break of DNA in Logic-Based Hypothesis Finding (Barthelemy Dworkin, Andrei Doncescu, Jean-Charles Faye and Katsumi Inoue)Probabilistic Logical Learning:The PITA System for Logical-Probabilistic Inference (Fabrizio Riguzzi and Terrance Swift)Learning a Generative Failure-Free PRISM Clause (Waleed Alsanie and James Cussens)Statistical Relational Learning of Object Affordances for Robotic Manipulation (Bogdan Moldovan, Martijn van Otterlo, Plinio Moreno, José Santos-Victor and Luc De Raedt)Learning from Linked Data by Markov Logic (Man Zhu and Zhiqiang Gao)Satisfiability Machines (Filip Železný)Implementations:Customisable Multi-Processor Acceleration of Inductive Logic Programming (Andreas K Fidjeland, Wayne Luk and Stephen H Muggleton)Multivalue Learning in ILP (Orlando Muoz Texzocotetla and Ren Mac Kinney Romero)Learning Dependent-Concepts in ILP: Application to Model-Driven Data Warehouses (Moez Essaidi, Aomar Osmani and Céline Rouveirol)Graph Contraction Pattern Matching for Graphs of Bounded Treewidth (Takashi Yamada and Takayoshi Shoudai)mLynx: Relational Mutual Information (Nicola Di Mauro, Teresa M A Basile, Stefano Ferilli and Floriana Esposito)Theory:Machine Learning Coalgebraic Proofs (Ekaterina Komendantskaya)Can ILP Deal with Incomplete and Vague Structured Knowledge? (Francesca A Lisi and Umberto Straccia)Logical Learning:Towards Efficient Higher-Order Logic Learning in a First-Order Datalog Framework (Niels Pahlavi and Stephen H Muggleton)Automatic Invention of Functional Abstractions (Robert J Henderson and Stephen H Muggleton)Constraints:Using Machine-Generated Soft Constraints for Roster Problems (Yoshihisa Shiina and Hayato Ohwada)Spatial and Temporal:Relational Learning for Football-Related Predictions (Jan Van Haaren and Guy Van den Broeck) Readership: Graduate students and researchers in the field of ILP, and academics and researchers in the fields of artificial intelligence and natural sciences. Key Features:Covers major areas of research in ILPProvides an up-to-date insight into the key sub-areas of implementation and theory of ILPThe papers in this volume do not appear in conference proceedings elsewhere in the literatureKeywords:Machine Learning;Logic Programs;Inductive Inference;Structure Learning;Relational Learning;Statistical Relational Learning

Foundations of Inductive Logic Programming

Foundations of Inductive Logic Programming PDF Author: Shan-Hwei Nienhuys-Cheng
Publisher: Springer Science & Business Media
ISBN: 9783540629276
Category : Computers
Languages : en
Pages : 404

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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.

Inductive Logic Programming

Inductive Logic Programming PDF Author: Filip Železný
Publisher: Springer Science & Business Media
ISBN: 3540859276
Category : Computers
Languages : en
Pages : 347

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Book Description
This book constitutes the refereed proceedings of the 18th International Conference on Inductive Logic Programming, ILP 2008, held in Prague, Czech Republic, in September 2008. The 20 revised full papers presented together with the abstracts of 5 invited lectures were carefully reviewed and selected during two rounds of reviewing and improvement from 46 initial submissions. All current topics in inductive logic programming are covered, ranging from theoretical and methodological issues to advanced applications. The papers present original results in the first-order logic representation framework, explore novel logic induction frameworks, and address also new areas such as statistical relational learning, graph mining, or the semantic Web.

Inductive Logic Programming

Inductive Logic Programming PDF Author: David Page
Publisher: Springer Science & Business Media
ISBN: 9783540647386
Category : Computers
Languages : en
Pages : 299

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Book Description
This book constitutes the refereed proceedings of the 8th International Conference on Inductive Logic Programming, ILP-98, held in Madison, Wisconsin, USA, in July 1998. The 27 revised full papers presented together with the abstracts of three invited talks were carefully reviewed and selected for inclusion in the book. All relevant aspects of inductive logic programming are covered ranging from theory to implementations and applications.

Inductive Logic Programming

Inductive Logic Programming PDF Author: Francesco Bergadano
Publisher: MIT Press
ISBN: 9780262023931
Category : Computers
Languages : en
Pages : 240

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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

Inductive Logic Programming

Inductive Logic Programming PDF Author: Celine Rouveirol
Publisher: Springer Science & Business Media
ISBN: 3540425381
Category : Computers
Languages : en
Pages : 259

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Book Description
This book constitutes the refereed proceedings of the 11th International Conference on Inductive Logic Programming, ILP 2001, held in Strasbourg, France in September 2001. The 21 revised full papers presented were carefully reviewed and selected from 37 submissions. Among the topics addressed are data mining issues for multi-relational databases, supervised learning, inductive inference, Bayesian reasoning, learning refinement operators, neural network learning, constraint satisfaction, genetic algorithms, statistical machine learning, transductive inference, etc.

Inductive Logic Programming

Inductive Logic Programming PDF Author: Germany) Ilp 200 (2005 Bonn
Publisher: Springer Science & Business Media
ISBN: 3540281770
Category : Computers
Languages : en
Pages : 425

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Book Description
This book constitutes the refereed proceedings of the 15th International Conference on Inductive Logic Programming, ILP 2005, held in Bonn, Germany, in August 2005. The 24 revised full papers presented together with the abstract of 4 invited lectures were carefully reviewed and selected for inclusion in the book. The papers address all current topics in inductive logic programming, ranging from theoretical and methodological issues to advanced applications in various areas, also including more diverse forms of non-propositional learning.