Probabilistic Inductive Logic Programming - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Probabilistic Inductive Logic Programming write by Luc De Raedt. This book was released on 2008-03-14. Probabilistic Inductive Logic Programming available in PDF, EPUB and Kindle. 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.
An Inductive Logic Programming Approach to Statistical Relational Learning
An Inductive Logic Programming Approach to Statistical Relational Learning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook An Inductive Logic Programming Approach to Statistical Relational Learning write by Kristian Kersting. This book was released on 2006. An Inductive Logic Programming Approach to Statistical Relational Learning available in PDF, EPUB and Kindle. 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.
Inductive Logic Programming
Inductive Logic Programming - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Inductive Logic Programming write by Stephen Muggleton. This book was released on 2007-07-27. Inductive Logic Programming available in PDF, EPUB and Kindle. This book constitutes the thoroughly refereed post-proceedings of the 16th International Conference on Inductive Logic Programming, ILP 2006, held in Santiago de Compostela, Spain, in August 2006. The papers address all current topics in inductive logic programming, ranging from theoretical and methodological issues to advanced applications.
Foundations of Probabilistic Logic Programming
Foundations of Probabilistic Logic Programming - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Foundations of Probabilistic Logic Programming write by Fabrizio Riguzzi. This book was released on 2023-07-07. Foundations of Probabilistic Logic Programming available in PDF, EPUB and Kindle. 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.
Inductive Logic Programming
Inductive Logic Programming - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Inductive Logic Programming write by Nicolas Lachiche. This book was released on 2018-03-19. Inductive Logic Programming available in PDF, EPUB and Kindle. 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.
