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

2006, ISBN: 3540352945

[EAN: 9783540352945], Neubuch, [SC: 0.0], [PU: Springer Berlin Heidelberg], INTELLIGENZ / KÜNSTLICHE INTELLIGENZ; KI; - AI; LERNEN LERNTHEORIE, LERNFORSCHUNG; CLUSTERINGSTABILITY; SUPPORT… Mehr…

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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 Editor
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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 Editor - neues Buch

2006, ISBN: 9783540352945

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 rev… Mehr…

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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, Band 4005) - Simon, Hans Ulrich, Lugosi, G??bor
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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, Band 4005) - Taschenbuch

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ISBN: 9783540352945

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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, Band 4005) - Simon, Hans Ulrich, Lugosi, G??bor
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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, Band 4005) - Taschenbuch

2008, ISBN: 9783540352945

Springer, Taschenbuch, Auflage: 2006, 676 Seiten, Publiziert: 2008-06-13T00:00:01Z, Produktgruppe: Buch, 2.06 kg, Informatik, IT-Ausbildung & -Berufe, Computer & Internet, Kategorien, Büc… Mehr…

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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, Band 4005) - Simon, Hans Ulrich, Lugosi, G??bor
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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, Band 4005) - Taschenbuch

2008, ISBN: 9783540352945

Springer, Taschenbuch, Auflage: 2006, 676 Seiten, Publiziert: 2008-06-13T00:00:01Z, Produktgruppe: Buch, 2.06 kg, Informatik, IT-Ausbildung & -Berufe, Computer & Internet, Kategorien, Büc… Mehr…

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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 Editor
Autor:
Titel:
ISBN-Nummer:

9783540352945


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.

Detailangaben zum Buch - Learning Theory: 19th Annual Conference on Learning Theory, COLT 2006, Pittsburgh, PA, USA, June 22-25, 2006, Proceedings Hans Ulrich Simon Editor


EAN (ISBN-13): 9783540352945
ISBN (ISBN-10): 3540352945
Taschenbuch
Erscheinungsjahr: 2006
Herausgeber: Springer Berlin Heidelberg Core >1 >T
656 Seiten
Gewicht: 1,005 kg
Sprache: eng/Englisch

Buch in der Datenbank seit 2007-04-30T04:12:38+02:00 (Berlin)
Detailseite zuletzt geändert am 2024-02-24T18:50:53+01:00 (Berlin)
ISBN/EAN: 9783540352945

ISBN - alternative Schreibweisen:
3-540-35294-5, 978-3-540-35294-5
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: hans ulrich simon, bor, gabor, lugosi, hans see, lugo, colt, hans ulrich berlin
Titel des Buches: june 2006, colt, the sciences the artificial, 19th annual lecture, science the artificial, usa and away, learning how ask, pittsburgh

Daten vom Verlag:

Autor/in: Hans Ulrich Simon; Gábor Lugosi
Titel: Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence; Learning Theory - 19th Annual Conference on Learning Theory, COLT 2006, Pittsburgh, PA, USA, June 22-25, 2006, Proceedings
Verlag: Springer; Springer Berlin
660 Seiten
Erscheinungsjahr: 2006-06-12
Berlin; Heidelberg; DE
Sprache: Englisch
106,99 € (DE)
109,99 € (AT)
118,00 CHF (CH)
Available
XII, 660 p.

BC; Hardcover, Softcover / Informatik, EDV/Informatik; Künstliche Intelligenz; Verstehen; Informatik; Clustering stability; Support Vector Machine; algorithmic learning; classification; computational learning; decision theory; game theory; inductive inference; kernel method; kernel methods; learning methods; machine learning; reinforcement learning; stability; supervised learning; algorithm analysis and problem complexity; Artificial Intelligence; Theory of Computation; Algorithms; Formal Languages and Automata Theory; Theoretische Informatik; Algorithmen und Datenstrukturen; EA

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.

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