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Algorithmic Learning Theory: 24th International Conference, Alt 2013, Singapore, October 6-9, 2013, Proceedings

Algorithmic Learning Theory: 24th International Conference, Alt 2013, Singapore, October 6-9, 2013, Proceedings (Paperback, 2013)

(24th International Conference, Alt 2013, Singapore, October 6-9, 2013, Proceedings)

R굆i Munos (지은이), Sanjay Jain, Frank Stephan (엮은이)
Springer-Verlag New York Inc
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Algorithmic Learning Theory: 24th International Conference, Alt 2013, Singapore, October 6-9, 2013, Proceedings
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책 정보

· 제목 : Algorithmic Learning Theory: 24th International Conference, Alt 2013, Singapore, October 6-9, 2013, Proceedings (Paperback, 2013) (24th International Conference, Alt 2013, Singapore, October 6-9, 2013, Proceedings)
· 분류 : 외국도서 > 컴퓨터 > 컴퓨터 공학
· ISBN : 9783642409349
· 쪽수 : 397쪽
· 출판일 : 2013-09-11

목차

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 Nystrom Method.- Nonparametric Multiple Change Point Estimation in Highly Dependent Time Series.

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