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Inductive Logic Programming [electronic resource] : 27th International Conference, ILP 2017, Orléans, France, September 4-6, 2017, Revised Selected Papers / edited by Nicolas Lachiche, Christel Vrain.

Contributor(s): Lachiche, Nicolas [editor.] | Vrain, Christel [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Artificial Intelligence: 10759Publisher: Cham : Springer International Publishing : Imprint: Springer, 2018Edition: 1st ed. 2018.Description: X, 185 p. 101 illus., 7 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319780900.Subject(s): Machine theory | Artificial intelligence | Compilers (Computer programs) | Computer science | Computer programming | Formal Languages and Automata Theory | Artificial Intelligence | Compilers and Interpreters | Computer Science Logic and Foundations of Programming | Programming TechniquesAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 005.131 Online resources: Click here to access online
Contents:
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.
In: Springer Nature eBookSummary: 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.
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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.

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.

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