Inductive Logic Programming: 27th International Conference

Inductive Logic Programming: 27th International Conference, ILP 2017, Orléans, France, September 4-6, 2017, Revised Selected Papers (Lecture Notes in Computer Science)

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

Table of Contents

Chapter 1. Relational Affordance Learning for Task-Dependent Robot Grasping
Chapter 2. Positive and Unlabeled Relational Classification Through Label Frequency Estimation
Chapter 3. On Applying Probabilistic Logic Programming to Breast Cancer Data
Chapter 4. Logical Vision: One-Shot Meta-Interpretive Learning from Real Images
Chapter 5. Demystifying Relational Latent Representations
Chapter 6. Parallel Online Learning of Event Definitions
Chapter 7. Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach
Chapter 8. Parallel Inductive Logic Programming System for Superlinear Speedup
Chapter 9. Inductive Learning from State Transitions over Continuous Domains
Chapter 10. Stacked Structure Learning for Lifted Relational Neural Networks
Chapter 11. Pruning Hypothesis Spaces Using Learned Domain Theories
Chapter 12. An Investigation into the Role of Domain-Knowledge on the Use of Embeddings