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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, 2010. The con… Mehr…
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Algorithmic Learning Theory 21st International Conference, ALT 2010, Canberra, Australia, October 6-8, 2010. Proceedings - gebrauchtes Buch
2010, ISBN: 9783642161070
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Algorithmic Learning Theory 21st International Conference, ALT 2010, Canberra, Australia, October 6-8, 2010. Proceedings - gebrauchtes Buch
2010, ISBN: 9783642161070
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2010, ISBN: 9783642161070
21st International Conference, ALT 2010, Canberra, Australia, October 6-8, 2010. Proceedings, Buch, Softcover, [PU: Springer Berlin], Springer Berlin, 2010
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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, 2010. The con… Mehr…
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, 2010. The con… Mehr…
Algorithmic Learning Theory 21st International Conference, ALT 2010, Canberra, Australia, October 6-8, 2010. Proceedings - gebrauchtes Buch
2010
ISBN: 9783642161070
[PU: Springer Berlin], Neubindung, Buchrücken leicht angestoßen 9254923/12, DE, [SC: 0.00], gebraucht; sehr gut, gewerbliches Angebot, 2010, Banküberweisung, PayPal, Klarna-Sofortüberweis… Mehr…
Algorithmic Learning Theory 21st International Conference, ALT 2010, Canberra, Australia, October 6-8, 2010. Proceedings - gebrauchtes Buch
2010, ISBN: 9783642161070
[PU: Springer Berlin], Neubindung, Buchrücken leicht angestoßen 9254923/12, DE, [SC: 0.00], gebraucht; sehr gut, gewerbliches Angebot, 2010, PayPal, Klarna-Sofortüberweisung, Internationa… Mehr…
2010, ISBN: 9783642161070
21st International Conference, ALT 2010, Canberra, Australia, October 6-8, 2010. Proceedings, Buch, Softcover, [PU: Springer Berlin], Springer Berlin, 2010
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Detailangaben zum Buch - Algorithmic Learning Theory by Marcus Hutter Paperback | Indigo Chapters
EAN (ISBN-13): 9783642161070
ISBN (ISBN-10): 3642161073
Gebundene Ausgabe
Taschenbuch
Erscheinungsjahr: 2010
Herausgeber: Marcus Hutter
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 2024-03-12T09:05:25+01:00 (Berlin)
ISBN/EAN: 9783642161070
ISBN - alternative Schreibweisen:
3-642-16107-3, 978-3-642-16107-0
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: hutter, vladimir, frank thomas, hütter, thomas stephan, hütte, marcus stephan, thomas alt
Titel des Buches: canberra, australia our, lecture notes computer science, hutter, learning englisch
Daten vom Verlag:
Autor/in: Marcus Hutter; Frank Stephan; Vladimir Vovk; Thomas Zeugmann
Titel: Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence; 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
Sprache: Englisch
53,49 € (DE)
54,99 € (AT)
59,00 CHF (CH)
Available
XIII, 421 p. 45 illus.
BC; 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; Artificial Intelligence; Programming Techniques; Formal Languages and Automata Theory; Algorithms; Theory of Computation; Computer Science Logic and Foundations of Programming; Computerprogrammierung und Softwareentwicklung; Theoretische Informatik; Algorithmen und Datenstrukturen; 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 KernelLearning: Algorithms and Mistake Bounds.- An Identity for Kernel Ridge Regression.unique visibility, state-of-the-art survey, fast-track conference proceedings
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