Learning Theory 19th Annual Conference on Learning Theory, COLT 2006, Pittsburgh, PA, USA, June 22-25, 2006, Proceedings
| Other Authors: | , |
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| Format: | eBook |
| Language: | English |
| Published: |
Berlin, Heidelberg
Springer Berlin Heidelberg
2006, 2006
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| Edition: | 1st ed. 2006 |
| Series: | Lecture Notes in Artificial Intelligence
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| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- Aggregation and Sparsity Via ?1 Penalized Least Squares
- A Randomized Online Learning Algorithm for Better Variance Control
- Online Prediction and Reinforcement Learning I
- Online Learning with Variable Stage Duration
- Online Learning Meets Optimization in the Dual
- Online Tracking of Linear Subspaces
- Online Multitask Learning
- Online Prediction and Reinforcement Learning II
- The Shortest Path Problem Under Partial Monitoring
- Tracking the Best Hyperplane with a Simple Budget Perceptron
- Logarithmic Regret Algorithms for Online Convex Optimization
- Online Variance Minimization
- Online Prediction and Reinforcement Learning III
- Online Learning with Constraints
- Continuous Experts and the Binning Algorithm
- Competing with Wild Prediction Rules
- Learning Near-Optimal Policies with Bellman-Residual Minimization Based Fitted Policy Iteration and a Single Sample Path
- Other Approaches
- Ranking with a P-Norm Push
- Subset Ranking Using Regression
- Invited Presentations
- Random Multivariate Search Trees
- On Learning and Logic
- Predictions as Statements and Decisions
- Clustering, Un-, and Semisupervised Learning
- A Sober Look at Clustering Stability
- PAC Learning Axis-Aligned Mixtures of Gaussians with No Separation Assumption
- Stable Transductive Learning
- Uniform Convergence of Adaptive Graph-Based Regularization
- Statistical Learning Theory
- The Rademacher Complexity of Linear Transformation Classes
- Function Classes That Approximate the Bayes Risk
- Functional Classification with Margin Conditions
- Significance and Recovery of Block Structures in Binary Matrices with Noise
- Regularized Learning and Kernel Methods
- Maximum Entropy Distribution Estimation with Generalized Regularization
- Unifying Divergence Minimization and Statistical Inference Via Convex Duality
- Mercer’s Theorem, Feature Maps, and Smoothing
- Learning Bounds for Support Vector Machines with Learned Kernels
- Query Learning and Teaching
- On Optimal Learning Algorithms for Multiplicity Automata
- Exact Learning Composed Classes with a Small Number of Mistakes
- DNF Are Teachable in the Average Case
- Teaching Randomized Learners
- Inductive Inference
- Memory-Limited U-Shaped Learning
- On Learning Languages from Positive Data and a Limited Number of Short Counterexamples
- Learning Rational Stochastic Languages
- Parent Assignment Is Hard for the MDL, AIC, and NML Costs
- Learning Algorithms and Limitations on Learning
- Uniform-Distribution Learnability of Noisy Linear Threshold Functions with Restricted Focus of Attention
- Discriminative Learning Can Succeed Where Generative Learning Fails
- Improved Lower Bounds for Learning Intersections of Halfspaces
- Efficient Learning Algorithms Yield Circuit Lower Bounds
- OnlineAggregation
- Optimal Oracle Inequality for Aggregation of Classifiers Under Low Noise Condition
- Active Sampling for Multiple Output Identification
- Improving Random Projections Using Marginal Information
- Open Problems
- Efficient Algorithms for General Active Learning
- Can Entropic Regularization Be Replaced by Squared Euclidean Distance Plus Additional Linear Constraints