Learning Theory: 19th Annual Conference on Learning Theory, COLT 2006, Pittsburgh, PA, USA, June 22-25, 2006, Proceedings - Couverture souple

Simon, Hans Ulrich ; Lugosi, G??bor

9783540352945: Learning Theory: 19th Annual Conference on Learning Theory, COLT 2006, Pittsburgh, PA, USA, June 22-25, 2006, Proceedings

Couverture souple

ISBN 10 : 3540352945 ISBN 13 : 9783540352945

Editeur : Springer, 2008

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This book constitutes the refereed proceedings of the 19th Annual Conference on Learning Theory, COLT 2006, held in Pittsburgh, Pennsylvania, USA, June 2006.

The book presents 43 revised full papers together with 2 articles on open problems and 3 invited lectures. The papers cover a wide range of topics including clustering, un- and semi-supervised learning, statistical learning theory, regularized learning and kernel methods, query learning and teaching, inductive inference, and more.

Les informations fournies dans la section � Synopsis � peuvent faire r�f�rence � une autre �dition de ce titre.

�diteur: Springer
Date d'�dition: 2008
Langue: anglais
ISBN 10: 3540352945
ISBN 13: 9783540352945
Reliure: Broch�
Nombre de pages: 676
Coordonn�es du fabricant: ProductSafety@springernature.com
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Learning Theory: 19th Annual Conference on Learning Theory, COLT 2006, Pittsburgh, PA, USA, June 22-25, 2006, Proceedings (Lecture Notes in Computer Science)

Simon, Hans Ulrich [Editor]; Lugosi, G�bor [Editor];

Edit� par Springer, 2008

ISBN 10 : 3540352945 ISBN 13 : 9783540352945

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Paperback. Etat : Good. Cover semi-creased. Small notations inside front, corner of text block moderately bent, else gentle wear. No markings of text noted. N� de r�f. du vendeur 44312a

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Learning Theory: 19th Annual Conference on Learning Theory, COLT 2006, Pittsburgh, PA, USA, June 22-25, 2006, Proceedings (Lecture Notes in Computer Science, 4005)

Simon, Hans Ulrich; Lugosi, G??bor

Edit� par Springer, 2006

ISBN 10 : 3540352945 ISBN 13 : 9783540352945

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Learning Theory

G�bor Lugosi

Edit� par Springer Berlin Heidelberg Jun 2006, 2006

ISBN 10 : 3540352945 ISBN 13 : 9783540352945

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book constitutes the refereed proceedings of the 19th Annual Conference on Learning Theory, COLT 2006, held in Pittsburgh, Pennsylvania, USA, June 2006. The book presents 43 revised full papers together with 2 articles on open problems and 3 invited lectures. The papers cover a wide range of topics including clustering, un- and semi-supervised learning, statistical learning theory, regularized learning and kernel methods, query learning and teaching, inductive inference, and more. 676 pp. Englisch. N� de r�f. du vendeur 9783540352945

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Learning Theory : 19th Annual Conference on Learning Theory, Colt 2006, Pittsburgh, Pa, USA, June 22-25, 2006, Proceedings

Lugosi, Gabor (EDT)

Edit� par Springer, 2006

ISBN 10 : 3540352945 ISBN 13 : 9783540352945

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Learning Theory

Simon, Hans Ulrich|Lugosi, G�bor

Edit� par Springer Berlin Heidelberg, 2006

ISBN 10 : 3540352945 ISBN 13 : 9783540352945

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Proceedings of the 19th Annual Conference on Learning Theory, COLT 2006Presents 43 revised full papers together with 2 articles on open problems and 3 invited lecturesCoverage includes clustering, un- and semi-supervised learning, statistic. N� de r�f. du vendeur 4888226

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Learning Theory

Simon, Hans Ulrich; Lugosi, G??bor

Edit� par Springer, 2006

ISBN 10 : 3540352945 ISBN 13 : 9783540352945

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Etat : New. pp. 676. N� de r�f. du vendeur 26349049

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Learning Theory

Simon, Hans Ulrich; Lugosi, G??bor

Edit� par Springer, 2006

ISBN 10 : 3540352945 ISBN 13 : 9783540352945

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Etat : New. Print on Demand pp. 676 Illus. N� de r�f. du vendeur 7531686

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Learning Theory | 19th Annual Conference on Learning Theory, COLT 2006, Pittsburgh, PA, USA, June 22-25, 2006, Proceedings

Hans Ulrich Simon (u. a.)

Edit� par Springer, 2006

ISBN 10 : 3540352945 ISBN 13 : 9783540352945

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Learning Theory

Simon Hans Ulrich Lugosi G?bor

Edit� par Springer, 2006

ISBN 10 : 3540352945 ISBN 13 : 9783540352945

Neuf Couverture souple

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Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne

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Etat : New. PRINT ON DEMAND pp. 676. N� de r�f. du vendeur 18349043

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Learning Theory

Hans Ulrich Simon

Edit� par Springer, Springer Jun 2006, 2006

ISBN 10 : 3540352945 ISBN 13 : 9783540352945

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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Invited Presentations.- Random Multivariate Search Trees.- On Learning and Logic.- Predictions as Statements and Decisions.- Clustering, Un-, and Semisupervised Learning.- A Sober Look at Clustering Stability.- PAC Learning Axis-Aligned Mixtures of Gaussians with No Separation Assumption.- Stable Transductive Learning.- Uniform Convergence of Adaptive Graph-Based Regularization.- Statistical Learning Theory.- The Rademacher Complexity of Linear Transformation Classes.- Function Classes That Approximate the Bayes Risk.- Functional Classification with Margin Conditions.- Significance and Recovery of Block Structures in Binary Matrices with Noise.- Regularized Learning and Kernel Methods.- Maximum Entropy Distribution Estimation with Generalized Regularization.- Unifying Divergence Minimization and Statistical Inference Via Convex Duality.- Mercer's Theorem, Feature Maps, and Smoothing.- Learning Bounds for Support Vector Machines with Learned Kernels.- Query Learning and Teaching.- On Optimal Learning Algorithms for Multiplicity Automata.- Exact Learning Composed Classes with a Small Number of Mistakes.- DNF Are Teachable in the Average Case.- Teaching Randomized Learners.- Inductive Inference.- Memory-Limited U-Shaped Learning.- On Learning Languages from Positive Data and a Limited Number of Short Counterexamples.- Learning Rational Stochastic Languages.- Parent Assignment Is Hard for the MDL, AIC, and NML Costs.- Learning Algorithms and Limitations on Learning.- Uniform-Distribution Learnability of Noisy Linear Threshold Functions with Restricted Focus of Attention.- Discriminative Learning Can Succeed Where Generative Learning Fails.- Improved Lower Bounds for Learning Intersections of Halfspaces.- Efficient Learning Algorithms Yield Circuit Lower Bounds.- OnlineAggregation.- Optimal Oracle Inequality for Aggregation of Classifiers Under Low Noise Condition.- Aggregation and Sparsity Via 1 Penalized Least Squares.- A Randomized Online Learning Algorithm for Better Variance Control.- Online Prediction and Reinforcement Learning I.- Online Learning with Variable Stage Duration.- Online Learning Meets Optimization in the Dual.- Online Tracking of Linear Subspaces.- Online Multitask Learning.- Online Prediction and Reinforcement Learning II.- The Shortest Path Problem Under Partial Monitoring.- Tracking the Best Hyperplane with a Simple Budget Perceptron.- Logarithmic Regret Algorithms for Online Convex Optimization.- Online Variance Minimization.- Online Prediction and Reinforcement Learning III.- Online Learning with Constraints.- Continuous Experts and the Binning Algorithm.- Competing with Wild Prediction Rules.- Learning Near-Optimal Policies with Bellman-Residual Minimization Based Fitted Policy Iteration and a Single Sample Path.- Other Approaches.- Ranking with a P-Norm Push.- Subset Ranking Using Regression.- Active Sampling for Multiple Output Identification.- Improving Random Projections Using Marginal Information.- Open Problems.- Efficient Algorithms for General Active Learning.- Can Entropic Regularization Be Replaced by Squared Euclidean Distance Plus Additional Linear Constraints.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 676 pp. Englisch. N� de r�f. du vendeur 9783540352945

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