Algorithmic Learning Theory (eBook, PDF)

27th International Conference, ALT 2016, Bari, Italy, October 19-21, 2016, Proceedings
Redaktion: Ortner, Ronald; Zilles, Sandra; Simon, Hans Ulrich

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: Springer-Verlag GmbH
• Seitenzahl: 371
• Erscheinungstermin: 12. Oktober 2016
• Englisch
• ISBN-13: 9783319463797
• Artikelnr.: 46927802

Inhaltsangabe

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.