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Inductive logic programming = 27th International Conference, ILP 2017, Orleans, France, September 4-6, 2017 : revised selected papers /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Inductive logic programming/ edited by Nicolas Lachiche, Christel Vrain.
Reminder of title:	27th International Conference, ILP 2017, Orleans, France, September 4-6, 2017 : revised selected papers /
remainder title:	ILP 2017
other author:	Lachiche, Nicolas.
corporate name:	ILP (Conference)
Published:	Cham :Springer International Publishing : : 2018.,
Description:	x, 185 p. :ill., digital ;24 cm.
[NT 15003449]:	Relational Affordance Learning for Task-dependent Robot Grasping -- Positive and Unlabeled Relational Classification Through Label Frequency Estimation -- On Applying Probabilistic Logic Programming to Breast Cancer Data -- Logical Vision: One-Shot Meta-Interpretive Learning from Real Images -- Demystifying Relational Latent Representations -- Parallel Online Learning of Event Definitions -- Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach -- Parallel Inductive Logic Programming System for Super-linear Speedup -- Inductive Learning from State Transitions over Continuous Domains -- Stacked Structure Learning for Lifted Relational Neural Networks -- Pruning Hypothesis Spaces Using Learned Domain Theories -- An Investigation into the Role of Domain-knowledge on the Use of Embeddings.
Contained By:	Springer eBooks
Subject:	Logic programming - Congresses. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-78090-0
ISBN:	9783319780900

Inductive logic programming = 27th International Conference, ILP 2017, Orleans, France, September 4-6, 2017 : revised selected papers /
Inductive logic programming27th International Conference, ILP 2017, Orleans, France, September 4-6, 2017 : revised selected papers /[electronic resource] :ILP 2017edited by Nicolas Lachiche, Christel Vrain. - Cham :Springer International Publishing :2018. - x, 185 p. :ill., digital ;24 cm. - Lecture notes in computer science,107590302-9743 ;. - Lecture notes in computer science ;10759..

Relational Affordance Learning for Task-dependent Robot Grasping -- Positive and Unlabeled Relational Classification Through Label Frequency Estimation -- On Applying Probabilistic Logic Programming to Breast Cancer Data -- Logical Vision: One-Shot Meta-Interpretive Learning from Real Images -- Demystifying Relational Latent Representations -- Parallel Online Learning of Event Definitions -- Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach -- Parallel Inductive Logic Programming System for Super-linear Speedup -- Inductive Learning from State Transitions over Continuous Domains -- Stacked Structure Learning for Lifted Relational Neural Networks -- Pruning Hypothesis Spaces Using Learned Domain Theories -- An Investigation into the Role of Domain-knowledge on the Use of Embeddings.

This book constitutes the thoroughly refereed post-conference proceedings of the 27th International Conference on Inductive Logic Programming, ILP 2017, held in Orleans, 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.

ISBN: 9783319780900

Standard No.: 10.1007/978-3-319-78090-0doiSubjects--Topical Terms:

840448
Logic programming
--Congresses.

LC Class. No.: QA76.63 / .I47 2017

Dewey Class. No.: 005.115

Inductive logic programming = 27th International Conference, ILP 2017, Orleans, France, September 4-6, 2017 : revised selected papers /
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520 $a This book constitutes the thoroughly refereed post-conference proceedings of the 27th International Conference on Inductive Logic Programming, ILP 2017, held in Orleans, 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.
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