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Learning Theory : 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007, Proceedings - Taschenbuch

2007, ISBN: 3540729259

[EAN: 9783540729259], Nouveau livre, [SC: 8.99], [PU: Springer Berlin Heidelberg], LERNEN / COMPUTER, MEDIEN; INDUCTIVEINFERENCE; KERNELMETHOD; MACHINELEARNING; OPTIMIZATION; STABILITY; A… Mehr…

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Learning Theory 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007, Proceedings - Gentile, Claudio (Herausgeber); Bshouty, Nader (Herausgeber)
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Learning Theory 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007, Proceedings - neues Buch

2007, ISBN: 3540729259

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Learning Theory 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007, Proceedings - Gentile, Claudio (Herausgeber); Bshouty, Nader (Herausgeber)
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Gentile, Claudio (Herausgeber); Bshouty, Nader (Herausgeber):
Learning Theory 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007, Proceedings - neues Buch

2007

ISBN: 3540729259

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Learning Theory: 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007, Proceed - neues Buch

2007, ISBN: 9783540729259

This book constitutes the refereed proceedings of the 20th Annual Conference on Learning Theory, COLT 2007, held in San Diego, CA, USA in June 2007. It covers unsupervised, semisupervised… Mehr…

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Nader Bshouty; Claudio Gentile:
Learning Theory - Taschenbuch

2007, ISBN: 9783540729259

20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007, Proceedings, Buch, Softcover, [PU: Springer Berlin], Springer Berlin, 2007

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Learning Theory: 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007, Proceed
Autor:
Titel:
ISBN-Nummer:

9783540729259


This book constitutes the refereed proceedings of the 20th Annual Conference on Learning Theory, COLT 2007, held in San Diego, CA, USA in June 2007. The 41 revised full papers presented together with 5 articles on open problems and 2 invited lectures were carefully reviewed and selected from a total of 92 submissions. The papers cover a wide range of topics and are organized in topical sections on unsupervised, semisupervised and active learning, statistical learning theory, inductive inference, regularized learning, kernel methods, SVM, online and reinforcement learning, learning algorithms and limitations on learning, dimensionality reduction, other approaches, and open problems.

Detailangaben zum Buch - Learning Theory: 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007, Proceed


EAN (ISBN-13): 9783540729259
ISBN (ISBN-10): 3540729259
Gebundene Ausgabe
Taschenbuch
Erscheinungsjahr: 2007
Herausgeber: Springer Berlin
634 Seiten
Gewicht: 0,903 kg
Sprache: eng/Englisch

Buch in der Datenbank seit 2007-07-30T18:33:07+02:00 (Berlin)
Detailseite zuletzt geändert am 2022-04-07T11:53:29+02:00 (Berlin)
ISBN/EAN: 9783540729259

ISBN - alternative Schreibweisen:
3-540-72925-9, 978-3-540-72925-9
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: gentile, nader, gentil, colt
Titel des Buches: annual, colt, the san diego, theory, bildfolien learning, usa and away, say this the usa, lecture notes artificial intelligence, june june

Daten vom Verlag:

Autor/in: Nader Bshouty; Claudio Gentile
Titel: Lecture Notes in Artificial Intelligence; Lecture Notes in Computer Science; Learning Theory - 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007, Proceedings
Verlag: Springer; Springer Berlin
636 Seiten
Erscheinungsjahr: 2007-06-01
Berlin; Heidelberg; DE
Sprache: Englisch
106,99 € (DE)
109,99 € (AT)
118,00 CHF (CH)
Available
XII, 636 p.

BC; Hardcover, Softcover / Informatik, EDV/Informatik; Künstliche Intelligenz; Verstehen; Informatik; Alphabet; active learning; algorithm; algorithmic learning; algorithms; classification; complexity; computational learning; decision theory; game theory; inductive inference; kernel method; machine learning; optimization; stability; algorithm analysis and problem complexity; Artificial Intelligence; Theory of Computation; Algorithms; Formal Languages and Automata Theory; Theoretische Informatik; Algorithmen und Datenstrukturen; EA

Invited Presentations.- Property Testing: A Learning Theory Perspective.- Spectral Algorithms for Learning and Clustering.- Unsupervised, Semisupervised and Active Learning I.- Minimax Bounds for Active Learning.- Stability of k-Means Clustering.- Margin Based Active Learning.- Unsupervised, Semisupervised and Active Learning II.- Learning Large-Alphabet and Analog Circuits with Value Injection Queries.- Teaching Dimension and the Complexity of Active Learning.- Multi-view Regression Via Canonical Correlation Analysis.- Statistical Learning Theory.- Aggregation by Exponential Weighting and Sharp Oracle Inequalities.- Occam’s Hammer.- Resampling-Based Confidence Regions and Multiple Tests for a Correlated Random Vector.- Suboptimality of Penalized Empirical Risk Minimization in Classification.- Transductive Rademacher Complexity and Its Applications.- Inductive Inference.- U-Shaped, Iterative, and Iterative-with-Counter Learning.- Mind Change Optimal Learning of Bayes Net Structure.- Learning Correction Grammars.- Mitotic Classes.- Online and Reinforcement Learning I.- Regret to the Best vs. Regret to the Average.- Strategies for Prediction Under Imperfect Monitoring.- Bounded Parameter Markov Decision Processes with Average Reward Criterion.- Online and Reinforcement Learning II.- On-Line Estimation with the Multivariate Gaussian Distribution.- Generalised Entropy and Asymptotic Complexities of Languages.- Q-Learning with Linear Function Approximation.- Regularized Learning, Kernel Methods, SVM.- How Good Is a Kernel When Used as a Similarity Measure?.- Gaps in Support Vector Optimization.- Learning Languages with Rational Kernels.- Generalized SMO-Style Decomposition Algorithms.- Learning Algorithms and Limitations on Learning.- Learning Nested Halfspaces and UphillDecision Trees.- An Efficient Re-scaled Perceptron Algorithm for Conic Systems.- A Lower Bound for Agnostically Learning Disjunctions.- Sketching Information Divergences.- Competing with Stationary Prediction Strategies.- Online and Reinforcement Learning III.- Improved Rates for the Stochastic Continuum-Armed Bandit Problem.- Learning Permutations with Exponential Weights.- Online and Reinforcement Learning IV.- Multitask Learning with Expert Advice.- Online Learning with Prior Knowledge.- Dimensionality Reduction.- Nonlinear Estimators and Tail Bounds for Dimension Reduction in l 1 Using Cauchy Random Projections.- Sparse Density Estimation with ?1 Penalties.- ?1 Regularization in Infinite Dimensional Feature Spaces.- Prediction by Categorical Features: Generalization Properties and Application to Feature Ranking.- Other Approaches.- Observational Learning in Random Networks.- The Loss Rank Principle for Model Selection.- Robust Reductions from Ranking to Classification.- Open Problems.- Rademacher Margin Complexity.- Open Problems in Efficient Semi-supervised PAC Learning.- Resource-Bounded Information Gathering for Correlation Clustering.- Are There Local Maxima in the Infinite-Sample Likelihood of Gaussian Mixture Estimation?.- When Is There a Free Matrix Lunch?.

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