 Название: Inductive Logic Programming: 27th International Conference, ILP 2017, Orl?ans, France, September 4-6, 2017, Revised Selected Papers
Автор: Nicolas Lachiche, Christel Vrain
Издательство: Springer
Год: 2018
ISBN: 9783319780894
Серия: Lecture Notes in Computer Science (Book 10759)
Формат: pdf
Страниц: 185
Размер: 11,8 mb
Язык: English
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.
Relational Affordance Learning for Task-Dependent Robot Grasping
Laura Antanas, Anton Dries, Plinio Moreno, and Luc De Raedt
Positive and Unlabeled Relational Classification Through Label Frequency Estimation
Jessa Bekker and Jesse Davis
On Applying Probabilistic Logic Programming to Breast Cancer Data
Joana C?rte-Real, In?s Dutra, and Ricardo Rocha
Logical Vision: One-Shot Meta-Interpretive Learning from Real Images
Wang-Zhou Dai, Stephen Muggleton, Jing Wen,
Alireza Tamaddoni-Nezhad, and Zhi-Hua Zhou Demystifying Relational Latent Representations
Sebastijan Duman?i? and Hendrik Blockeel
Parallel Online Learning of Event Definitions
Nikos Katzouris, Alexander Artikis, and Georgios Paliouras
Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach
Navdeep Kaur, Gautam Kunapuli, Tushar Khot, Kristian Kersting, William Cohen, and Sriraam Natarajan
Parallel Inductive Logic Programming System for Superlinear Speedup
Hiroyuki Nishiyama and Hayato Ohwada
Inductive Learning from State Transitions over Continuous Domains
Tony Ribeiro, Sophie Tourret, Maxime Folschette, Morgan Magnin, Domenico Borzacchiello, Francisco Chinesta, Olivier Roux, and Katsumi Inoue
Stacked Structure Learning for Lifted Relational Neural Networks
Gustav ?ourek, Martin Svato?, Filip ?elezn?, Steven Schockaert, and Ond?ej Ku?elka
Pruning Hypothesis Spaces Using Learned Domain Theories
Martin Svato?, Gustav ?ourek, Filip ?elezn?, Steven Schockaert, and Ond?ej Ku?elka
An Investigation into the Role of Domain-Knowledge on the Use of Embeddings
Lovekesh Vig, Ashwin Srinivasan, Michael Bain, and Ankit Verma
Author Index
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Inductive Logic Programming: 27th International Conference, ILP 2017, Orl?ans, France, September 4-6, 2017, Revised Selected Papers 
