Ortner / Simon / Zilles Algorithmic Learning Theory
1. Auflage 2016
ISBN: 978-3-319-46379-7
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
27th International Conference, ALT 2016, Bari, Italy, October 19-21, 2016, Proceedings
E-Book, Englisch, 371 Seiten
Reihe: Computer Science (R0)
ISBN: 978-3-319-46379-7
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
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.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
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.
