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Author | : Luc De Raedt |
Publisher | : Springer |
Total Pages | : 348 |
Release | : 2008-02-26 |
Genre | : Computers |
ISBN | : 354078652X |
Download Probabilistic Inductive Logic Programming Book in PDF, ePub and Kindle
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.
Author | : Kristian Kersting |
Publisher | : IOS Press |
Total Pages | : 258 |
Release | : 2006 |
Genre | : Computers |
ISBN | : 9781586036744 |
Download An Inductive Logic Programming Approach to Statistical Relational Learning Book 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.
Author | : Fabrizio Riguzzi |
Publisher | : River Publishers |
Total Pages | : 422 |
Release | : 2018-09-01 |
Genre | : Computers |
ISBN | : 8770220182 |
Download Foundations of Probabilistic Logic Programming Book in PDF, ePub and Kindle
Probabilistic Logic Programming extends Logic Programming by enabling the representation of uncertain information. Probabilistic Logic Programming is at the intersection of two wider research fields: the integration of logic and probability and Probabilistic Programming. Logic enables the representation of complex relations among entities while probability theory is useful for model uncertainty over attributes and relations. Combining the two is a very active field of study. Probabilistic Programming extends programming languages with probabilistic primitives that can be used to write complex probabilistic models. Algorithms for the inference and learning tasks are then provided automatically by the system. Probabilistic Logic programming is at the same time a logic language, with its knowledge representation capabilities, and a Turing complete language, with its computation capabilities, thus providing the best of both worlds. 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. Foundations of Probabilistic Logic Programming 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.
Author | : Luc De Raedt |
Publisher | : Springer Science & Business Media |
Total Pages | : 348 |
Release | : 2008-03-14 |
Genre | : Computers |
ISBN | : 3540786511 |
Download Probabilistic Inductive Logic Programming Book 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.
Author | : Stephen Muggleton |
Publisher | : Springer Science & Business Media |
Total Pages | : 466 |
Release | : 2007-07-27 |
Genre | : Computers |
ISBN | : 3540738460 |
Download Inductive Logic Programming Book 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.
Author | : Stephen Muggleton |
Publisher | : |
Total Pages | : 412 |
Release | : 2014-01-15 |
Genre | : |
ISBN | : 9783662186947 |
Download Inductive Logic Programming Book in PDF, ePub and Kindle
Author | : Nicolas Lachiche |
Publisher | : Springer |
Total Pages | : 185 |
Release | : 2018-03-19 |
Genre | : Mathematics |
ISBN | : 3319780905 |
Download Inductive Logic Programming Book 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.
Author | : Fabrizio Riguzzi |
Publisher | : Springer |
Total Pages | : 173 |
Release | : 2018-08-24 |
Genre | : Computers |
ISBN | : 3319999605 |
Download Inductive Logic Programming Book in PDF, ePub and Kindle
This book constitutes the refereed conference proceedings of the 28th International Conference on Inductive Logic Programming, ILP 2018, held in Ferrara, Italy, in September 2018. The 10 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.
Author | : Stephen Muggleton |
Publisher | : Springer |
Total Pages | : 456 |
Release | : 2007-09-20 |
Genre | : Computers |
ISBN | : 3540738479 |
Download Inductive Logic Programming Book 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.
Author | : Fabrizio Riguzzi |
Publisher | : Springer |
Total Pages | : 283 |
Release | : 2013-06-04 |
Genre | : Mathematics |
ISBN | : 3642388124 |
Download Inductive Logic Programming Book in PDF, ePub and Kindle
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.