Learning Theory - neues Buch

EAN (ISBN-13): 9783540729273
Erscheinungsjahr: 2007
Herausgeber: Springer Science+Business Media

Buch in der Datenbank seit 2016-11-23T18:58:14+01:00 (Berlin)
Detailseite zuletzt geändert am 2019-12-02T11:30:44+01:00 (Berlin)
ISBN/EAN: 9783540729273

ISBN - alternative Schreibweisen:
978-3-540-72927-3
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: gentile
Titel des Buches: learning

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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: 2007-06-12
Berlin; Heidelberg; DE
Sprache: Englisch
96,29 € (DE)
99,00 € (AT)
118,00 CHF (CH)
Available
XII, 636 p.

EA; E107; eBook; Nonbooks, PBS / Informatik, EDV/Informatik; Künstliche Intelligenz; Verstehen; 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; C; Artificial Intelligence; Theory of Computation; Algorithms; Formal Languages and Automata Theory; Computer Science; Theoretische Informatik; Algorithmen und Datenstrukturen; BC

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?.