Computational Learning Theory: 4th European Conference, EuroCOLT'99 Nordkirchen, Germany, March 29-31, 1999 Proceedings

ISBN-13: 9783540657019 / Angielski / Miękka / 1999 / 299 str.

Paul Fischer;Hans U. Simon

Opis

This text presents the proceedings of the 4th European Conference on Computational Learning Theory. The 23 contributions address topics such as learning from queries and counter examples, reinforcement learning, online learning and export advice, teaching and learning and inductive inference.

Spis treści

Invited Lectures.- Theoretical Views of Boosting.- Open Theoretical Questions in Reinforcement Learning.- Learning from Random Examples.- A Geometric Approach to Leveraging Weak Learners.- Query by Committee, Linear Separation and Random Walks.- Hardness Results for Neural Network Approximation Problems.- Learning from Queries and Counterexamples.- 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.- Reinforcement Learning.- On the Asymptotic Behavior of a Constant Stepsize Temporal-Difference Learning Algorithm.- On-line Learning and Expert Advice.- Direct and Indirect Algorithms for On-line Learning of Disjunctions.- Averaging Expert Predictions.- Teaching and Learning.- On Teaching and Learning Intersection-Closed Concept Classes.- Inductive Inference.- Avoiding Coding Tricks by Hyperrobust Learning.- Mind Change Complexity of Learning Logic Programs.- Statistical Theory of Learning and Pattern Recognition.- 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.- Margin Distribution Bounds on Generalization.- Generalization Performance of Classifiers in Terms of Observed Covering Numbers.- Entropy Numbers, Operators and Support Vector Kernels.