Download or read book An Inductive Logic Programming Approach to Statistical Relational Learning written by Kristian Kersting and published by IOS Press. This book was released on 2006 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Talks about Logic Programming, Uncertainty Reasoning and Machine Learning. This book includes definitions that circumscribe the area formed by extending Inductive Logic Programming to cases annotated with probability values. It investigates the approach of Learning from proofs and the issue of upgrading Fisher Kernels to Relational Fisher Kernels.
Download or read book Introduction to Statistical Relational Learning written by Lise Getoor and published by MIT Press. This book was released on 2019-09-22 with total page 602 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different proposed approaches, and applications. Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases and programming languages to represent structure. In Introduction to Statistical Relational Learning, leading researchers in this emerging area of machine learning describe current formalisms, models, and algorithms that enable effective and robust reasoning about richly structured systems and data. The early chapters provide tutorials for material used in later chapters, offering introductions to representation, inference and learning in graphical models, and logic. The book then describes object-oriented approaches, including probabilistic relational models, relational Markov networks, and probabilistic entity-relationship models as well as logic-based formalisms including Bayesian logic programs, Markov logic, and stochastic logic programs. Later chapters discuss such topics as probabilistic models with unknown objects, relational dependency networks, reinforcement learning in relational domains, and information extraction. By presenting a variety of approaches, the book highlights commonalities and clarifies important differences among proposed approaches and, along the way, identifies important representational and algorithmic issues. Numerous applications are provided throughout.
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.
Download or read book Inductive Logic Programming written by Katsumi Inoue and published by Springer. This book was released on 2016-06-25 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the 25th International Conference on Inductive Logic Programming, ILP 2015, held in Kyoto, Japan, in August 2015. The 14 revised papers presented were carefully reviewed and selected from 44 submissions. The papers focus on topics such as theories, algorithms, representations and languages, systems and applications of ILP, and cover all areas of learning in logic, relational learning, relational data mining, statistical relational learning, multi-relational data mining, relational reinforcement learning, graph mining, connections with other learning paradigms, among others.
Download or read book Probabilistic Inductive Logic Programming written by Luc De Raedt and published by Springer Science & Business Media. This book was released on 2008-03-14 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Inductive Logic Programming written by Filip Železný and published by Springer. This book was released on 2008-08-29 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 18th International Conference on Inductive Logic Programming was held in Prague, September 10–12, 2008. ILP returned to Prague after 11 years, and it is tempting to look at how the topics of interest have evolved during that time. The ILP community clearly continues to cherish its beloved ?rst-order logic representation framework. This is legitimate, as the work presented at ILP 2008 demonstrated that there is still room for both extending established ILP approaches (such as inverse entailment) and exploring novel logic induction frameworks (such as brave induction). Besides the topics lending ILP research its unique focus, we were glad to see in this year’s proceedings a good n- ber of papers contributing to areas such as statistical relational learning, graph mining, or the semantic web. To help open ILP to more mainstream research areas, the conference featured three excellent invited talks from the domains of the semantic web (Frank van Harmelen), bioinformatics (Mark Craven) and cognitive sciences (Josh Tenenbaum). We deliberately looked for speakers who are not directly involved in ILP research. We further invited a tutorial on stat- tical relational learning (Kristian Kersting) to meet the strong demand to have the topic presented from the ILP perspective. Lastly, Stefano Bertolo from the European Commission was invited to give a talk on the ideal niches for ILP in the current EU-supported research on intelligent content and semantics.
Download or read book Handbook of Relational Learning written by Ashwin Srinivasan and published by CRC Press. This book was released on 2014-01-15 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: With increased interest in relational learning and the growing importance of machine learning, artificial intelligence, and data mining, inductive logic programming (ILP)—at the boundary between machine learning and logic programming—is on the rise. Authored by a leading researcher in the field, this timely book provides the first comprehensive introduction to be published in over ten years. It uses an accessible approach to present key concepts in ILP and provide an overview of possible applications. The book covers important topics in the field, including probability and statistics, statistical relational learning, experimental design, and combinatorial algorithms.
Download or read book Inductive Logic Programming written by Hendrik Blockeel and published by Springer Science & Business Media. This book was released on 2008-03-14 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Inductive Logic Programming written by Gerson Zaverucha and published by Springer. This book was released on 2014-09-23 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-proceedings of the 23rd International Conference on Inductive Logic Programming, ILP 2013, held in Rio de Janeiro, Brazil, in August 2013. The 9 revised extended papers were carefully reviewed and selected from 42 submissions. The conference now focuses on all aspects of learning in logic, multi-relational learning and data mining, statistical relational learning, graph and tree mining, relational reinforcement learning, and other forms of learning from structured data.
Download or read book Inductive Logic Programming written by Nicolas Lachiche and published by Springer. This book was released on 2018-03-19 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the 27th International Conference on Inductive Logic Programming, ILP 2017, held in Orléans, France, in September 2017. The 12 full papers presented were carefully reviewed and selected from numerous submissions. Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data.
Download or read book Statistical Relational Artificial Intelligence written by Luc De Raedt and published by Morgan & Claypool Publishers. This book was released on 2016-03-24 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks.
Download or read book Inductive Logic Programming written by Stephen H. Muggleton and published by Springer Nature. This book was released on with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Inductive Logic Programming written by James Cussens and published by Springer. This book was released on 2017-07-15 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the 26th International Conference on Inductive Logic Programming, ILP 2016, held in London, UK, in September 2016. The 10 full papers presented were carefully reviewed and selected from 29 submissions. The papers represent well the current breath of ILP research topics such as predicate invention; graph-based learning; spatial learning; logical foundations; statistical relational learning; probabilistic ILP; implementation and scalability; applications in robotics, cyber security and games.
Download or read book Inductive Logic Programming written by Elena Bellodi and published by Springer Nature. This book was released on 2023-12-21 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 32nd International Conference on Inductive Logic Programming, ILP 2023, held in Bari, Italy, during November 13–15, 2023. The 11 full papers and 1 short paper included in this book were carefully reviewed and selected from 18 submissions. They cover all aspects of learning in logic, multi-relational data mining, statistical relational learning, graph and tree mining, learning in other (non-propositional) logic-based knowledge representation frameworks, exploring intersections to statistical learning and other probabilistic approaches.
Download or read book Latest Advances in Inductive Logic Programming written by Stephen H Muggleton and published by World Scientific. This book was released on 2014-10-30 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Inductive Logic Programming written by Stefan Kramer and published by Springer. This book was released on 2005-08-29 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: 1 “Change is inevitable.” Embracing this quote we have tried to carefully exp- iment with the format of this conference, the 15th International Conference on Inductive Logic Programming, hopefully making it even better than it already was. But it will be up to you, the inquisitive reader of this book, to judge our success. The major changes comprised broadening the scope of the conference to include more diverse forms of non-propositional learning, to once again have tutorials on exciting new areas, and, for the ?rst time, to also have a discovery challenge as a platform for collaborative work. This year the conference was co-located with ICML 2005, the 22nd Inter- tional Conference on Machine Learning, and also in close proximity to IJCAI 2005, the 19th International Joint Conference on Arti?cial Intelligence. - location can be tricky, but we greatly bene?ted from the local support provided by Codrina Lauth, Michael May, and others. We were also able to invite all ILP and ICML participants to shared events including a poster session, an invited talk, and a tutorial about the exciting new area of “statistical relational lea- ing”. Two more invited talks were exclusively given to ILP participants and were presented as a kind of stock-taking—?ttingly so for the 15th event in a series—but also tried to provide a recipe for future endeavours.
