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Inductive logic programming = 28th I...
~
Zese, Riccardo.
Inductive logic programming = 28th International Conference, ILP 2018, Ferrara, Italy, September 2-4, 2018 : proceedings /
Record Type:
Language materials, printed : Monograph/item
Title/Author:
Inductive logic programming/ edited by Fabrizio Riguzzi, Elena Bellodi, Riccardo Zese.
Reminder of title:
28th International Conference, ILP 2018, Ferrara, Italy, September 2-4, 2018 : proceedings /
remainder title:
ILP 2018
other author:
Riguzzi, Fabrizio.
corporate name:
Workshop on the Preservation of Stability under Discretization
Published:
Cham :Springer International Publishing : : 2018.,
Description:
ix, 173 p. :ill., digital ; : 24 cm.;
Contained By:
Springer eBooks
Subject:
Logic programming -
Online resource:
http://dx.doi.org/10.1007/978-3-319-99960-9
ISBN:
9783319999609
Inductive logic programming = 28th International Conference, ILP 2018, Ferrara, Italy, September 2-4, 2018 : proceedings /
Inductive logic programming
28th International Conference, ILP 2018, Ferrara, Italy, September 2-4, 2018 : proceedings /[electronic resource] :ILP 2018edited by Fabrizio Riguzzi, Elena Bellodi, Riccardo Zese. - Cham :Springer International Publishing :2018. - ix, 173 p. :ill., digital ;24 cm. - Lecture notes in computer science,111050302-9743 ;. - Lecture notes in computer science ;6140..
Derivation reduction of metarules in meta-interpretive learning -- Large-Scale Assessment of Deep Relational Machines -- How much can experimental cost be reduced in active learning of agent strategies? -- Diagnostics of Trains with Semantic Diagnostics Rules -- The game of Bridge: a challenge for ILP -- Sampling-Based SAT/ASP Multi-Model Optimization as a Framework for Probabilistic Inference -- Explaining Black-box Classifiers with ILP - Empowering LIME with Aleph to Approximate Non-linear Decisions with Relational Rules -- Learning Dynamics with Synchronous, Asynchronous and General Semantics -- Was the Year 2000 a Leap Year? Step-wise Narrowing Theories with Metagol -- Targeted End-to-end Knowledge Graph Decomposition.
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.
ISBN: 9783319999609
Standard No.: 10.1007/978-3-319-99960-9doiSubjects--Topical Terms:
568059
Logic programming
LC Class. No.: QA76.63 / .I47 2018
Dewey Class. No.: 005.115
Inductive logic programming = 28th International Conference, ILP 2018, Ferrara, Italy, September 2-4, 2018 : proceedings /
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28th International Conference, ILP 2018, Ferrara, Italy, September 2-4, 2018 : proceedings /
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Derivation reduction of metarules in meta-interpretive learning -- Large-Scale Assessment of Deep Relational Machines -- How much can experimental cost be reduced in active learning of agent strategies? -- Diagnostics of Trains with Semantic Diagnostics Rules -- The game of Bridge: a challenge for ILP -- Sampling-Based SAT/ASP Multi-Model Optimization as a Framework for Probabilistic Inference -- Explaining Black-box Classifiers with ILP - Empowering LIME with Aleph to Approximate Non-linear Decisions with Relational Rules -- Learning Dynamics with Synchronous, Asynchronous and General Semantics -- Was the Year 2000 a Leap Year? Step-wise Narrowing Theories with Metagol -- Targeted End-to-end Knowledge Graph Decomposition.
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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.
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