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Computational Learning Theory: 4th European Conference, EuroCOLT'99 Nordkirchen, Germany, March 29-31, 1999 Proceedings: Lecture Notes in Computer Science, cartea 1572

Editat de Paul Fischer, Hans U. Simon
en Limba Engleză Paperback – 17 mar 1999

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Specificații

ISBN-13: 9783540657019
ISBN-10: 3540657010
Pagini: 324
Ilustrații: X, 299 p.
Dimensiuni: 155 x 235 x 17 mm
Greutate: 0.45 kg
Ediția:1999
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

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.

Caracteristici

Includes supplementary material: sn.pub/extras