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Computational Learning Theory [electronic resource] : 15th Annual Conference on Computational Learning Theory, COLT 2002, Sydney, Australia, July 8-10, 2002. Proceedings /

Contributor(s): Material type: TextTextSeries: Lecture Notes in Artificial Intelligence ; 2375Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2002Edition: 1st ed. 2002Description: XII, 412 p. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783540454359
Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification:
  • 006.3 23
LOC classification:
  • Q334-342
  • TA347.A78
Online resources:
Contents:
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.
In: Springer Nature eBook
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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.

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