Fabrizio Riguzzi - Böcker
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5 produkter
5 produkter
Del 9336 - Lecture Notes in Computer Science
AI*IA 2015 Advances in Artificial Intelligence
XIVth International Conference of the Italian Association for Artificial Intelligence, Ferrara, Italy, September 23-25, 2015, Proceedings
Häftad, Engelska, 2015
551 kr
Skickas inom 10-15 vardagar
This book constitutes the refereed proceedings of the 14th International Conference of the Italian Association for Artificial Intelligence, A*IA 2015, held in Ferrara, Italy, in September 2015. The 35 full papers presented were carefully reviewed and selected from 44 submissions.
Del 11105 - Lecture Notes in Computer Science
Inductive Logic Programming
28th International Conference, ILP 2018, Ferrara, Italy, September 2–4, 2018, Proceedings
Häftad, Engelska, 2018
551 kr
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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.
Inductive Logic Programming
22nd International Conference, ILP 2012, Dubrovnik, Croatia, September 16-18,2012, Revised Selected papers
Häftad, Engelska, 2013
508 kr
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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.
Foundations of Probabilistic Logic Programming
Languages, Semantics, Inference and Learning
Inbunden, Engelska, 2018
1 292 kr
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Probabilistic Logic Programming extends Logic Programming by enabling the representation of uncertain information by means of probability theory. 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.
Foundations of Probabilistic Logic Programming
Languages, Semantics, Inference and Learning
Inbunden, Engelska, 2023
1 598 kr
Skickas inom 10-15 vardagar
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.