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Inductive logic programming = 29th International Conference, ILP 2019, Plovdiv, Bulgaria, September 3-5, 2019 : proceedings /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Inductive logic programming/ edited by Dimitar Kazakov, Can Erten.
Reminder of title:	29th International Conference, ILP 2019, Plovdiv, Bulgaria, September 3-5, 2019 : proceedings /
remainder title:	ILP 2019
other author:	Kazakov, Dimitar.
corporate name:	ILP (Conference)
Published:	Cham :Springer International Publishing : : 2020.,
Description:	ix, 145 p. :ill., digital ;24 cm.
[NT 15003449]:	CONNER: A Concurrent ILP Learner in Description Logic -- Towards Meta-interpretive Learning of Programming Language Semantics -- Towards an ILP Application in Machine Ethics -- On the Relation Between Loss Functions and T-Norms -- Rapid Restart Hill Climbing for Learning Description Logic Concepts -- Neural Networks for Relational Data -- Learning Logic Programs from Noisy State Transition Data -- A New Algorithm for Computing Least Generalization of a Set of Atoms -- LazyBum: Decision Tree Learning Using Lazy Propositionalization -- Weight Your Words: the Effect of Different Weighting Schemes on Wordification Performance -- Learning Probabilistic Logic Programs over Continuous Data.
Contained By:	Springer Nature eBook
Subject:	Logic programming - Congresses. -
Online resource:	https://doi.org/10.1007/978-3-030-49210-6
ISBN:	9783030492106

Inductive logic programming = 29th International Conference, ILP 2019, Plovdiv, Bulgaria, September 3-5, 2019 : proceedings /
Inductive logic programming29th International Conference, ILP 2019, Plovdiv, Bulgaria, September 3-5, 2019 : proceedings /[electronic resource] :ILP 2019edited by Dimitar Kazakov, Can Erten. - Cham :Springer International Publishing :2020. - ix, 145 p. :ill., digital ;24 cm. - Lecture notes in computer science,117700302-9743 ;. - Lecture notes in computer science ;11770..

CONNER: A Concurrent ILP Learner in Description Logic -- Towards Meta-interpretive Learning of Programming Language Semantics -- Towards an ILP Application in Machine Ethics -- On the Relation Between Loss Functions and T-Norms -- Rapid Restart Hill Climbing for Learning Description Logic Concepts -- Neural Networks for Relational Data -- Learning Logic Programs from Noisy State Transition Data -- A New Algorithm for Computing Least Generalization of a Set of Atoms -- LazyBum: Decision Tree Learning Using Lazy Propositionalization -- Weight Your Words: the Effect of Different Weighting Schemes on Wordification Performance -- Learning Probabilistic Logic Programs over Continuous Data.

This book constitutes the refereed conference proceedings of the 29th International Conference on Inductive Logic Programming, ILP 2019, held in Plovdiv, Bulgaria, in September 2019. The 11 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: 9783030492106

Standard No.: 10.1007/978-3-030-49210-6doiSubjects--Topical Terms:

840448
Logic programming
--Congresses.

LC Class. No.: QA76.63 / .I47 2019

Dewey Class. No.: 005.115

Inductive logic programming = 29th International Conference, ILP 2019, Plovdiv, Bulgaria, September 3-5, 2019 : proceedings /
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520 $a This book constitutes the refereed conference proceedings of the 29th International Conference on Inductive Logic Programming, ILP 2019, held in Plovdiv, Bulgaria, in September 2019. The 11 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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