Markov Logic
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Structural Syntactic And Statistical Pattern Recognition
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Author : Niels da Vitoria Lobo
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-11-24
Structural Syntactic And Statistical Pattern Recognition written by Niels da Vitoria Lobo and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-11-24 with Computers categories.
This book constitutes the refereed proceedings of the 12th International Workshop on Structural and Syntactic Pattern Recognition, SSPR 2008 and the 7th International Workshop on Statistical Techniques in Pattern Recognition, SPR 2008, held jointly in Orlando, FL, USA, in December 2008 as a satellite event of the 19th International Conference of Pattern Recognition, ICPR 2008. The 56 revised full papers and 42 revised poster papers presented together with the abstracts of 4 invited papers were carefully reviewed and selected from 175 submissions. The papers are organized in topical sections on graph-based methods, probabilistic and stochastic structural models for PR, image and video analysis, shape analysis, kernel methods, recognition and classification, applications, ensemble methods, feature selection, density estimation and clustering, computer vision and biometrics, pattern recognition and applications, pattern recognition, as well as feature selection and clustering.
An Inductive Logic Programming Approach To Statistical Relational Learning
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Author : Kristian Kersting
language : en
Publisher: IOS Press
Release Date : 2006
An Inductive Logic Programming Approach To Statistical Relational Learning written by Kristian Kersting and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Computers categories.
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.
Probabilistic Inductive Logic Programming
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Author : Luc De Raedt
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-03-14
Probabilistic Inductive Logic Programming written by Luc De Raedt and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-03-14 with Computers categories.
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.
Ecai 2008
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Author : European Coordinating Committee for Artificial Intelligence
language : en
Publisher: IOS Press
Release Date : 2008
Ecai 2008 written by European Coordinating Committee for Artificial Intelligence and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Computers categories.
Includes subconference "Prestigious Applications of Intelligent Systems (PAIS 2008)."
Foundations Of Probabilistic Logic Programming
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Author : Fabrizio Riguzzi
language : en
Publisher: CRC Press
Release Date : 2023-07-07
Foundations Of Probabilistic Logic Programming written by Fabrizio Riguzzi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-07 with Computers categories.
Since its birth, the field of Probabilistic Logic Programming has seen a steady increase of activity, with many proposals for languages and algorithms for inference and learning. This book aims at providing an overview of the field with a special emphasis on languages under the Distribution Semantics, one of the most influential approaches. The book presents the main ideas for semantics, inference, and learning and highlights connections between the methods. Many examples of the book include a link to a page of the web application http://cplint.eu where the code can be run online. This 2nd edition aims at reporting the most exciting novelties in the field since the publication of the 1st edition. The semantics for hybrid programs with function symbols was placed on a sound footing. Probabilistic Answer Set Programming gained a lot of interest together with the studies on the complexity of inference. Algorithms for solving the MPE and MAP tasks are now available. Inference for hybrid programs has changed dramatically with the introduction of Weighted Model Integration. With respect to learning, the first approaches for neuro-symbolic integration have appeared together with algorithms for learning the structure for hybrid programs. Moreover, given the cost of learning PLPs, various works proposed language restrictions to speed up learning and improve its scaling.
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language : en
Publisher: IOS Press
Release Date :
written by and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Inductive Logic Programming
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Author : Filip Železný
language : en
Publisher: Springer
Release Date : 2008-08-29
Inductive Logic Programming written by Filip Železný and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-08-29 with Computers categories.
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.
Inductive Logic Programming
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Author : Fabrizio Riguzzi
language : en
Publisher: Springer
Release Date : 2013-06-04
Inductive Logic Programming written by Fabrizio Riguzzi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-04 with Mathematics categories.
This book constitutes the thoroughly refereed post-proceedings of the 22nd International Conference on Inductive Logic Programming, ILP 2012, held in Dubrovnik, Croatia, in September 2012. The 18 revised full papers were carefully reviewed and selected from 41 submissions. The papers cover the following topics: propositionalization, logical foundations, implementations, probabilistic ILP, applications in robotics and biology, grammatical inference, spatial learning and graph-based learning.
Markov Logic
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Author : Pedro Domingos
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2009
Markov Logic written by Pedro Domingos and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Computers categories.
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.
Introduction To Statistical Relational Learning
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Author : Lise Getoor
language : en
Publisher: MIT Press
Release Date : 2019-09-22
Introduction To Statistical Relational Learning written by Lise Getoor and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-22 with Computers categories.
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.