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Hutter, Marcus:

Algorithmic Learning Theory 21st International Conference, ALT 2010, Canberra, Australia, October 6-8, 2010. Proceedings Marcus Hutter (u. a.) Taschenbuch Lecture Notes in Computer Science - Taschenbuch

2010, ISBN: 9783642161070

[ED: Taschenbuch], [PU: Springer Berlin], This volume contains the papers presented at the 21st International Conf- ence on Algorithmic Learning Theory (ALT 2010), which was held in Canbe… Mehr…

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Hutter, Marcus:

Algorithmic Learning Theory 21st International Conference, ALT 2010, Canberra, Australia, October 6-8, 2010. Proceedings Marcus Hutter (u. a.) Taschenbuch Lecture Notes in Computer Science - Taschenbuch

2010, ISBN: 9783642161070

[ED: Taschenbuch], [PU: Springer Berlin], This volume contains the papers presented at the 21st International Conf- ence on Algorithmic Learning Theory (ALT 2010), which was held in Canbe… Mehr…

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Marcus Hutter:
Algorithmic Learning Theory - Taschenbuch

2010

ISBN: 9783642161070

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Algorithmic Learning Theory - Taschenbuch

2010, ISBN: 9783642161070

This volume contains the papers presented at the 21st International Conf- ence on Algorithmic Learning Theory (ALT 2010), which was held in Canberra, Australia, October 6–8, 201… Mehr…

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Marcus Hutter:
Algorithmic Learning Theory - Taschenbuch

2010, ISBN: 9783642161070

Paperback, [PU: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG], Constitutes the refereed proceedings of the 21th International Conference on Algorithmic Learning Theory, ALT 2010, t… Mehr…

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Details zum Buch
Algorithmic Learning Theory

This book constitutes the refereed proceedings of the 21th International Conference on Algorithmic Learning Theory, ALT 2010, held in Canberra, Australia, in October 2010, co-located with the 13th International Conference on Discovery Science, DS 2010. The 26 revised full papers presented together with the abstracts of 5 invited talks were carefully reviewed and selected from 44 submissions. The papers are divided into topical sections of papers on statistical learning; grammatical inference and graph learning; probably approximately correct learning; query learning and algorithmic teaching; on-line learning; inductive inference; reinforcement learning; and on-line learning and kernel methods.

Detailangaben zum Buch - Algorithmic Learning Theory


EAN (ISBN-13): 9783642161070
ISBN (ISBN-10): 3642161073
Gebundene Ausgabe
Taschenbuch
Erscheinungsjahr: 2010
Herausgeber: Springer Berlin
419 Seiten
Gewicht: 0,653 kg
Sprache: eng/Englisch

Buch in der Datenbank seit 2007-03-30T18:23:42+02:00 (Berlin)
Detailseite zuletzt geändert am 2022-06-16T10:48:00+02:00 (Berlin)
ISBN/EAN: 9783642161070

ISBN - alternative Schreibweisen:
3-642-16107-3, 978-3-642-16107-0


Daten vom Verlag:

Autor/in: Marcus Hutter; Frank Stephan; Vladimir Vovk; Thomas Zeugmann
Titel: Lecture Notes in Artificial Intelligence; Lecture Notes in Computer Science; Algorithmic Learning Theory - 21st International Conference, ALT 2010, Canberra, Australia, October 6-8, 2010. Proceedings
Verlag: Springer; Springer Berlin
421 Seiten
Erscheinungsjahr: 2010-09-27
Berlin; Heidelberg; DE
Gewicht: 0,656 kg
Sprache: Englisch
85,59 € (DE)
87,99 € (AT)
106,60 CHF (CH)
POD
XIII, 421 p. 45 illus.

BC; Book; Hardcover, Softcover / Informatik, EDV/Informatik; Künstliche Intelligenz; Verstehen; Informatik; algorithmic learning theory; algorithms; classification; complexity; complexity theory; decision trees; grammtical inference; inductive inference; kolmogorov complexity; logic programming; query learning; statistical learn; support vector machines; teaching models; unsupervised learning; algorithm analysis and problem complexity; C; Artificial Intelligence; Programming Techniques; Mathematical Logic and Formal Languages; Algorithm Analysis and Problem Complexity; Computation by Abstract Devices; Logics and Meanings of Programs; Artificial Intelligence; Programming Techniques; Formal Languages and Automata Theory; Algorithms; Theory of Computation; Computer Science Logic and Foundations of Programming; Computer Science; Computerprogrammierung und Softwareentwicklung; Theoretische Informatik; Algorithmen und Datenstrukturen; Theoretische Informatik; Theoretische Informatik; EA

Editors’ Introduction.- Editors’ Introduction.- Invited Papers.- Towards General Algorithms for Grammatical Inference.- The Blessing and the Curse of the Multiplicative Updates.- Discovery of Abstract Concepts by a Robot.- Contrast Pattern Mining and Its Application for Building Robust Classifiers.- Optimal Online Prediction in Adversarial Environments.- Regular Contributions.- An Algorithm for Iterative Selection of Blocks of Features.- Bayesian Active Learning Using Arbitrary Binary Valued Queries.- Approximation Stability and Boosting.- A Spectral Approach for Probabilistic Grammatical Inference on Trees.- PageRank Optimization in Polynomial Time by Stochastic Shortest Path Reformulation.- Inferring Social Networks from Outbreaks.- Distribution-Dependent PAC-Bayes Priors.- PAC Learnability of a Concept Class under Non-atomic Measures: A Problem by Vidyasagar.- A PAC-Bayes Bound for Tailored Density Estimation.- Compressed Learning with Regular Concept.- A Lower Bound for Learning Distributions Generated by Probabilistic Automata.- Lower Bounds on Learning Random Structures with Statistical Queries.- Recursive Teaching Dimension, Learning Complexity, and Maximum Classes.- Toward a Classification of Finite Partial-Monitoring Games.- Switching Investments.- Prediction with Expert Advice under Discounted Loss.- A Regularization Approach to Metrical Task Systems.- Solutions to Open Questions for Non-U-Shaped Learning with Memory Limitations.- Learning without Coding.- Learning Figures with the Hausdorff Metric by Fractals.- Inductive Inference of Languages from Samplings.- Optimality Issues of Universal Greedy Agents with Static Priors.- Consistency of Feature Markov Processes.- Algorithms for Adversarial Bandit Problems with Multiple Plays.- Online Multiple Kernel Learning: Algorithms and Mistake Bounds.- An Identity for Kernel Ridge Regression.

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