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Algorithmic Learning Theory - Herausgegeben:Munos, Rémi; Jain, Sanjay; Zeugmann, Thomas; Stephan, Frank
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Herausgegeben:Munos, Rémi; Jain, Sanjay; Zeugmann, Thomas; Stephan, Frank:

Algorithmic Learning Theory - Livro de bolso

2013, ISBN: 9783642409349

[ED: Softcover], [PU: Springer / Springer Berlin Heidelberg / Springer, Berlin], This book constitutes the proceedings of the 24th International Conference on Algorithmic Learning Theory,… mais…

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Algorithmic Learning Theory / 24th International Conference, ALT 2013, Singapore, October 6-9, 2013, Proceedings / Sanjay Jain (u. a.) / Taschenbuch / Lecture Notes in Artificial Intelligence / 2013 - Jain, Sanjay
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Jain, Sanjay:

Algorithmic Learning Theory / 24th International Conference, ALT 2013, Singapore, October 6-9, 2013, Proceedings / Sanjay Jain (u. a.) / Taschenbuch / Lecture Notes in Artificial Intelligence / 2013 - Livro de bolso

2013, ISBN: 9783642409349

[ED: Taschenbuch], [PU: Springer Berlin], This book constitutes the proceedings of the 24th International Conference on Algorithmic Learning Theory, ALT 2013, held in Singapore in October… mais…

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Algorithmic Learning Theory 24th International Conference, ALT 2013, Singapore, October 6-9, 2013, Proceedings Sanjay Jain (u. a.) Taschenbuch Lecture Notes in Artificial Intelligence 2013 - Jain, Sanjay
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Jain, Sanjay:
Algorithmic Learning Theory 24th International Conference, ALT 2013, Singapore, October 6-9, 2013, Proceedings Sanjay Jain (u. a.) Taschenbuch Lecture Notes in Artificial Intelligence 2013 - Livro de bolso

2013

ISBN: 9783642409349

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Algorithmic Learning Theory - Sanjay Jain
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Sanjay Jain:
Algorithmic Learning Theory - Livro de bolso

2013, ISBN: 9783642409349

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Algorithmic Learning Theory - Sanjay Jain; Rémi Munos; Frank Stephan; Thomas Zeugmann
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Sanjay Jain; Rémi Munos; Frank Stephan; Thomas Zeugmann:
Algorithmic Learning Theory - Livro de bolso

2013, ISBN: 9783642409349

24th International Conference, ALT 2013, Singapore, October 6-9, 2013, Proceedings, Buch, Softcover, [PU: Springer Berlin], Springer Berlin, 2013

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Algorithmic Learning Theory
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9783642409349


This book constitutes the proceedings of the 24th International Conference on Algorithmic Learning Theory, ALT 2013, held in Singapore in October 2013, and co-located with the 16th International Conference on Discovery Science, DS 2013. The 23 papers presented in this volume were carefully reviewed and selected from 39 submissions. In addition the book contains 3 full papers of invited talks. The papers are organized in topical sections named: online learning, inductive inference and grammatical inference, teaching and learning from queries, bandit theory, statistical learning theory, Bayesian/stochastic learning, and unsupervised/semi-supervised learning.

Dados detalhados do livro - Algorithmic Learning Theory


EAN (ISBN-13): 9783642409349
ISBN (ISBN-10): 3642409342
Livro de capa dura
Livro de bolso
Ano de publicação: 2013
Editor/Editora: Springer Berlin

Livro na base de dados desde 2014-03-19T08:58:57-03:00 (Sao Paulo)
Página de detalhes modificada pela última vez em 2023-10-02T11:52:38-03:00 (Sao Paulo)
Número ISBN/EAN: 9783642409349

Número ISBN - Ortografia alternativa:
3-642-40934-2, 978-3-642-40934-9
Ortografia alternativa e termos de pesquisa relacionados:
Autor do livro: remi, thomas stephan, thomas frank, jain
Título do livro: bildfolien learning, lecture notes artificial intelligence, october, jain

Dados da editora

Autor: Sanjay Jain; Rémi Munos; Frank Stephan; Thomas Zeugmann
Título: Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence; Algorithmic Learning Theory - 24th International Conference, ALT 2013, Singapore, October 6-9, 2013, Proceedings
Editora: Springer; Springer Berlin
397 Páginas
Ano de publicação: 2013-09-11
Berlin; Heidelberg; DE
Impresso / Feito em
Língua: Inglês
53,49 € (DE)
54,99 € (AT)
59,00 CHF (CH)
POD
XVIII, 397 p. 30 illus.

BC; Hardcover, Softcover / Informatik, EDV/Informatik; Künstliche Intelligenz; Verstehen; Informatik; combinatorial optimization; gradient algorithms; online learning; reinforcement learning; universal artificial intelligence; algorithm analysis and problem complexity; Artificial Intelligence; Formal Languages and Automata Theory; Algorithms; Theory of Computation; Computer Science Logic and Foundations of Programming; Automated Pattern Recognition; Theoretische Informatik; Algorithmen und Datenstrukturen; Mustererkennung; EA

Editors’ Introduction.- Learning and Optimizing with Preferences.- Efficient Algorithms for Combinatorial Online Prediction.- Exact Learning from Membership Queries: Some Techniques, Results and New Directions.- Online Learning Universal Algorithm for Trading in Stock Market Based on the Method of Calibration.- Combinatorial Online Prediction via Metarounding.- On Competitive Recommendations.- Online PCA with Optimal Regrets.- Inductive Inference and Grammatical Inference Partial Learning of Recursively Enumerable Languages.- Topological Separations in Inductive Inference.- PAC Learning of Some Subclasses of Context-Free Grammars with Basic Distributional Properties from Positive Data.- Universal Knowledge-Seeking Agents for Stochastic Environments.- Teaching and Learning from Queries Order Compression Schemes.- Learning a Bounded-Degree Tree Using Separator Queries.- Faster Hoeffding Racing: Bernstein Races via Jackknife Estimates.- Robust Risk-Averse Stochastic Multi-armed Bandits.- An Efficient Algorithm for Learning with Semi-bandit Feedback.- Differentially-Private Learning of Low Dimensional Manifolds.- Generalization and Robustness of Batched Weighted Average Algorithm with V-Geometrically Ergodic Markov Data.- Adaptive Metric Dimensionality Reduction.- Dimension-Adaptive Bounds on Compressive FLD Classification.- Bayesian Methods for Low-Rank Matrix Estimation: Short Survey and Theoretical Study.- Concentration and Confidence for Discrete Bayesian Sequence Predictors.- Algorithmic Connections between Active Learning and Stochastic Convex Optimization.- Unsupervised/Semi-Supervised Learning Unsupervised Model-Free Representation Learning.- Fast Spectral Clustering via the Nyström Method.- Nonparametric Multiple Change Point Estimation in Highly Dependent Time Series.
Conference proceedings of the International Conference on Algorithmic Learning Theory

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