Learning Theory 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA; June 13-15, 2007. Proceedings /
| Συγγραφή απο Οργανισμό/Αρχή: | |
|---|---|
| Άλλοι συγγραφείς: | , |
| Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
| Γλώσσα: | English |
| Έκδοση: |
Berlin, Heidelberg :
Springer Berlin Heidelberg,
2007.
|
| Σειρά: | Lecture Notes in Computer Science,
4539 |
| Θέματα: | |
| Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- 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?.
