ISBN: 9783540729259 - learning theory: 20th annual conference on learning theory, colt 2007, san diego, ca, usa, june 13-15, 2007, proceedings (lecture notes in computer ... notes in computer science, 4539, band 4539) (9 Ergebnisse)

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Taschenbuch. Zustand: Neu. Learning Theory | 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007, Proceedings | Nader Bshouty (u. a.) | Taschenbuch | xii | Englisch | 2007 | Springer | EAN 9783540729259 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 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, as well as open problems. 648 pp. Englisch.…

Kartoniert / Broschiert. Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 and active learning, statistical learning theory, inductive infere.…

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 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, as well as open problems. …

Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 .Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 648 pp. Englisch.…