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Inductive logic programming27th Inte...
~
(1998 :)
Inductive logic programming27th International Conference, ILP 2017, Orleans, France, September 4-6, 2017 : revised selected papers /
Record Type:
Electronic resources : Monograph/item
Title/Author:
Inductive logic programmingedited 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:
Published:
Cham :Springer International Publishing :2018.
Description:
x, 185 p. :ill., digital ;24 cm.
Contained By:
Springer eBooks
Subject:
Logic programming
Online resource:
http://dx.doi.org/10.1007/978-3-319-78090-0
ISBN:
9783319780900$q(electronic bk.)
Inductive logic programming27th International Conference, ILP 2017, Orleans, France, September 4-6, 2017 : revised selected papers /
Inductive logic programming
27th 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 ;4891..
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$q(electronic bk.)
Standard No.: 10.1007/978-3-319-78090-0doiSubjects--Topical Terms:
295961
Logic programming
LC Class. No.: QA76.63 / .I47 2017
Dewey Class. No.: 005.115
Inductive logic programming27th International Conference, ILP 2017, Orleans, France, September 4-6, 2017 : revised selected papers /
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27th International Conference, ILP 2017, Orleans, France, September 4-6, 2017 : revised selected papers /
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edited by Nicolas Lachiche, Christel Vrain.
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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.
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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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