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Inductive Logic Programming: 29th International Conference, ILP 2019, Plovdiv, Bulgaria, September 35, 2019, Proceedings 2020 ed. [Pehme köide]

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  • Formaat: Paperback / softback, 145 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, 19 Illustrations, color; 106 Illustrations, black and white; IX, 145 p. 125 illus., 19 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Artificial Intelligence 11770
  • Ilmumisaeg: 03-Jun-2020
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030492095
  • ISBN-13: 9783030492090
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Inductive Logic Programming: 29th International Conference, ILP 2019, Plovdiv, Bulgaria, September 35, 2019, Proceedings 2020 ed.Suurem pilt
  • Formaat: Paperback / softback, 145 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, 19 Illustrations, color; 106 Illustrations, black and white; IX, 145 p. 125 illus., 19 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Artificial Intelligence 11770
  • Ilmumisaeg: 03-Jun-2020
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030492095
  • ISBN-13: 9783030492090
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783030492090.html
Märksõnad:
This book constitutes the refereed conference proceedings of the 29th International Conference on Inductive Logic Programming, ILP 2019, held in Plovdiv, Bulgaria, in September 2019.





The 11 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.

CONNER: A Concurrent ILP Learner in Description Logic.- Towards Meta-interpretive Learning of Programming Language Semantics.- Towards an ILP Application in Machine Ethics.- On the Relation Between Loss Functions and T-Norms.- Rapid Restart Hill Climbing for Learning Description Logic Concepts.- Neural Networks for Relational Data.- Learning Logic Programs from Noisy State Transition Data.- A New Algorithm for Computing Least Generalization of a Set of Atoms.- LazyBum: Decision Tree Learning Using Lazy Propositionalization.- Weight Your Words: the Effect of Different Weighting Schemes on Wordification Performance.- Learning Probabilistic Logic Programs over Continuous Data.