計(jì)算學(xué)習(xí)理論/會(huì)議錄 Computational learning theory

內(nèi)容概要

This book constitutes the refereed proceedings of the 15th Annual Conference on Computational Learning Theory, COLT 2002, held in Sydney, Australia, in July 2002.The 26 revised full papers presented were carefully reviewed and selected from 55 submissions. The papers are organized in topical sections on statistical learning theory, online learning, inductive inference, PAC learning, boosting, and other learning paradigms.

書籍目錄

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 BoundsOnline 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 WinnowInductive 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 MeasurePAC 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 ConceptsBoosting  A Consistent Strategy for Boosting Algorithms  The Consistency of Greedy Algorithms for Classification  Maximizing the Margin with BoostingOther Learning Paradigms  Performance Guarantees for Hierarchical Clustering  Self-Optimizing and Pareto-Optimal Policies in General Environments Based on Bayes-Mixtures  Prediction and DimensionInvited TalkAuthor Index

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