Ortner / Simon / Zilles Algorithmic Learning Theory

27th International Conference, ALT 2016, Bari, Italy, October 19-21, 2016, Proceedings
1. Auflage 2016
ISBN: 978-3-319-46379-7
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 371 Seiten, eBook

Reihe: Lecture Notes in Artificial Intelligence

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.

Ortner, Ronald

Simon, Hans Ulrich

Zilles, Sandra

Inhaltsverzeichnis

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.