Algorithmic Learning Theory -

Algorithmic Learning Theory

26th International Conference, ALT 2015, Banff, AB, Canada, October 4-6, 2015, Proceedings
Buch | Softcover
XVII, 395 Seiten
2015 | 1st ed. 2015
Springer International Publishing (Verlag)
978-3-319-24485-3 (ISBN)
53,49 inkl. MwSt
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.

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.

Erscheinungsdatum
Reihe/Serie Lecture Notes in Artificial Intelligence
Lecture Notes in Computer Science
Zusatzinfo XVII, 395 p. 26 illus. in color.
Verlagsort Cham
Sprache englisch
Maße 155 x 235 mm
Themenwelt Informatik Datenbanken Data Warehouse / Data Mining
Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Schlagworte Active learning • Algorithmic Learning Theory • artificial intelligence (incl. robotics) • boolean function learning • Computational Complexity • Computer Science • Computer Science, general • data mining and knowledge discovery • generalization bounds • Inductive Inference • Kolmogorov complexity • machine learning theory • Markov Decision Processes • Models of learning • Online learning theory • pattern recognition • query learning • Regret bounds • Reinforcement Learning • sample complexity • Sample complexity and generalization bounds • Semi-Supervised Learning • statistical learning theory • Unsupervised Learning
ISBN-10 3-319-24485-X / 331924485X
ISBN-13 978-3-319-24485-3 / 9783319244853
Zustand Neuware
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