Computational learning theory : proceedings | Ben-David, Shai, Fischer, Paul, Vitányi, Paul M

Computational Learning Theory: 4th European Conference, EuroCOLT’99 Nordkirchen, Germany, March 29–31, 1999 Proceedings
Author: Paul Fischer, Hans Ulrich Simon
Published by Springer Berlin Heidelberg
ISBN: 978-3-540-65701-9
DOI: 10.1007/3-540-49097-3

Table of Contents:

• Theoretical Views of Boosting
• Open Theoretical Questions in Reinforcement Learning
• A Geometric Approach to Leveraging Weak Learners
• Query by Committee, Linear Separation and Random Walks
• Hardness Results for Neural Network Approximation Problems
• Learnability of Quantified Formulas
• Learning Multiplicity Automata from Smallest Counterexamples
• Exact Learning when Irrelevant Variables Abound
• An Application of Codes to Attribute-Efficient Learning
• Learning Range Restricted Horn Expressions
• On the Asymptotic Behavior of a Constant Stepsize Temporal-Difference Learning Algorithm
• Direct and Indirect Algorithms for On-line Learning of Disjunctions
• Averaging Expert Predictions
• On Teaching and Learning Intersection-Closed Concept Classes
• Avoiding Coding Tricks by Hyperrobust Learning
• Mind Change Complexity of Learning Logic Programs
• Regularized Principal Manifolds
• Distribution-Dependent Vapnik-Chervonenkis Bounds
• Lower Bounds on the Rate of Convergence of Nonparametric Pattern Recognition
• On Error Estimation for the Partitioning Classification Rule

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