Algorithmic Learning Theory

23rd International Conference, ALT 2012, Lyon, France, October 29-31, 2012, Proceedings

Nader H. Bshouty, Gilles Stoltz, Nicolas Vayatis, Thomas Zeugmann (Herausgeber)

Buch | Softcover

XII, 381 Seiten

2012 | 2012
Springer Berlin (Verlag)
978-3-642-34105-2 (ISBN)

Infos

This book constitutes the refereed proceedings of the 23rd International Conference on Algorithmic Learning Theory, ALT 2012, held in Lyon, France, in October 2012. The conference was co-located and held in parallel with the 15th International Conference on Discovery Science, DS 2012. The 23 full papers and 5 invited talks presented were carefully reviewed and selected from 47 submissions. The papers are organized in topical sections on inductive inference, teaching and PAC learning, statistical learning theory and classification, relations between models and data, bandit problems, online prediction of individual sequences, and other models of online learning.

Inhalt

inductive inference.- teaching and PAC learning.- statistical learning theory and classification.- relations between models and data.- bandit problems, online prediction of individual sequences.- other models of online learning.

Produkt-Details

• Erscheint lt. Verlag: 15.9.2012
• Reihe/Serie: Lecture Notes in Artificial Intelligence
• Reihe/Serie: Lecture Notes in Computer Science
• Zusatzinfo: XII, 381 p. 23 illus.
• Verlagsort: Berlin
• Sprache: englisch
• Maße: 155 x 235 mm
• Gewicht: 603 g
• Themenwelt: Informatik ► Software Entwicklung ► User Interfaces (HCI)
• Themenwelt: Informatik ► Theorie / Studium ► Algorithmen
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Schlagworte: Algorithm analysis and problem complexity • Computational Finance • domain adaptation • pattern mining • Reinforcement Learning • statistical learning theory
• ISBN-10: 3-642-34105-5 / 3642341055
• ISBN-13: 978-3-642-34105-2 / 9783642341052
• Zustand: Neuware