Computational Learning Theory

Statistical Learning Theory.- Agnostic Learning Nonconvex Function Classes.- Entropy, Combinatorial Dimensions and Random Averages.- Geometric Parameters of Kernel Machines.- Localized Rademacher Complexities.- Some Local Measures of Complexity of Convex Hulls and Generalization Bounds.- Online Learning.- Path Kernels and Multiplicative Updates.- Predictive Complexity and Information.- Mixability and the Existence of Weak Complexities.- A Second-Order Perceptron Algorithm.- Tracking Linear-Threshold Concepts with Winnow.- Inductive Inference.- Learning Tree Languages from Text.- Polynomial Time Inductive Inference of Ordered Tree Patterns with Internal Structured Variables from Positive Data.- Inferring Deterministic Linear Languages.- Merging Uniform Inductive Learners.- The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions.- PAC Learning.- New Lower Bounds for Statistical Query Learning.- Exploring Learnability between Exact and PAC.- PAC Bounds for Multi-armed Bandit and Markov Decision Processes.- Bounds for the Minimum Disagreement Problem with Applications to Learning Theory.- On the Proper Learning of Axis Parallel Concepts.- Boosting.- A Consistent Strategy for Boosting Algorithms.- The Consistency of Greedy Algorithms for Classification.- Maximizing the Margin with Boosting.- Other Learning Paradigms.- Performance Guarantees for Hierarchical Clustering.- Self-Optimizing and Pareto-Optimal Policies in General Environments Based on Bayes-Mixtures.- Prediction and Dimension.- Invited Talk.- Learning the Internet.

Includes supplementary material: sn.pub/extras