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
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Название: Inductive Logic Programming: 27th International Conference, ILP 2017, Orl?ans, France, September 4-6, 2017, Revised Selected Papers 