Inductive Logic Programming: 27th International Conference, ILP 2017, Orléans, France, September 4-6, 2017, Revised Selected Papers: Lecture Notes in Computer Science, cartea 10759
Editat de Nicolas Lachiche, Christel Vrainen Limba Engleză Paperback – 15 mar 2018
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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Specificații
ISBN-13: 9783319780894
ISBN-10: 3319780891
Pagini: 180
Ilustrații: X, 185 p. 101 illus., 7 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.28 kg
Ediția:1st ed. 2018
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence
Locul publicării:Cham, Switzerland
ISBN-10: 3319780891
Pagini: 180
Ilustrații: X, 185 p. 101 illus., 7 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.28 kg
Ediția:1st ed. 2018
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence
Locul publicării:Cham, Switzerland
Cuprins
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.