Algorithmic Learning Theory

26th International Conference, ALT 2015, Banff, AB, Canada, October 4-6, 2015, Proceedings
1. Auflage 2015
ISBN: 978-3-319-24485-3
Verlag: Springer International Publishing

Buch, Englisch, Band 9355, 395 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 6263 g

Reihe: Lecture Notes in Computer Science

This book constitutes the proceedings of the 26th International Conference on Algorithmic Learning Theory, ALT 2015, held in Banff, AB, Canada, in October 2015, and co-located with the 18th International Conference on Discovery Science, DS 2015. The 23 full papers presented in this volume were carefully reviewed and selected from 44 submissions. In addition the book contains 2 full papers summarizing the invited talks and 2 abstracts of invited talks. The papers are organized in topical sections named: inductive inference; learning from queries, teaching complexity; computational learning theory and algorithms; statistical learning theory and sample complexity; online learning, stochastic optimization; and Kolmogorov complexity, algorithmic information theory.

Zielgruppe

Research

Autoren/Hrsg.

Chaudhuri, Kamalika

Zilles, Sandra

GENTILE, CLAUDIO

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

Inductive inference.- Learning from queries, teaching complexity.- Computational learning theory and algorithms.- Statistical learning theory and sample complexity.- Online learning.- Stochastic optimization.- Kolmogorov complexity, algorithmic information theory.