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

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Bshouty, Nader (Editor ), Gentile, Claudio (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2007.
Edición:1st ed. 2007.
Colección:Lecture Notes in Artificial Intelligence, 4539
Temas:
Artificial intelligence.
Computer science.
Algorithms.
Machine theory.
Artificial Intelligence.
Theory of Computation.
Formal Languages and Automata Theory.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • 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 Uphill Decision 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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