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Algorithmic Learning Theory: Lecture Notes in Computer Science, cartea 9925

Editat de Ronald Ortner, Hans Ulrich Simon, Sandra Zilles
en Limba Engleză Paperback – 21 sep 2016
This book constitutes the refereed proceedings of the 27th International Conference on Algorithmic Learning Theory, ALT 2016, held in Bari, Italy, in October 2016, co-located with the 19th International Conference on Discovery Science, DS 2016. The 24 regular papers presented in this volume were carefully reviewed and selected from 45 submissions. In addition the book contains 5 abstracts of invited talks. The papers are organized in topical sections named: error bounds, sample compression schemes; statistical learning, theory, evolvability; exact and interactive learning; complexity of teaching models; inductive inference; online learning; bandits and reinforcement learning; and clustering.
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Specificații

ISBN-13: 9783319463780
ISBN-10: 3319463780
Pagini: 392
Ilustrații: XIX, 371 p. 21 illus.
Dimensiuni: 155 x 235 x 22 mm
Greutate: 0.59 kg
Ediția:1st edition 2016
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

Error bounds, sample compression schemes.- Statistical learning, theory, evolvability.- Exact and interactive learning.- Complexity of teaching models.- Inductive inference.- Online learning.- Bandits and reinforcement learning.- Clustering.

Caracteristici

Includes supplementary material: sn.pub/extras