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

Computational Learning Theory

4th European Conference, Eurocolt'99 Nordkirchen, Germany, March 29-31, 1999 Proceedings

By: Paul Fischer (Editor), Hans Ulrich Simon (Editor)

Paperback Published: 17th March 1999
ISBN: 9783540657019
Number Of Pages: 299

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This volume contains papers presented at the Fourth European Conference on ComputationalLearningTheory, whichwasheldatNordkirchenCastle, inNo- kirchen, NRW, Germany, from March 29 to 31, 1999. This conference is the fourth in a series of bi-annual conferences established in 1993. TheEuroCOLTconferencesarefocusedontheanalysisoflearningalgorithms and the theory of machine learning, and bring together researchers from a wide variety of related elds. Some of the issues and topics that are addressed include the sample and computational complexity of learning speci c model classes, frameworks modeling the interaction between the learner, teacher and the en- ronment (such as learning with queries, learning control policies and inductive inference), learningwithcomplexmodels(suchasdecisiontrees, neuralnetworks, and support vector machines), learning with minimal prior assumptions (such as mistake-bound models, universal prediction, and agnostic learning), and the study of model selection techniques. We hope that these conferences stimulate an interdisciplinary scienti c interaction that will be fruitful in all represented elds. Thirty- ve papers were submitted to the program committee for conside- tion, and twenty-one of these were accepted for presentation at the conference and publication in these proceedings. In addition, Robert Schapire (AT & T Labs), and Richard Sutton (AT & T Labs) were invited to give lectures and contribute a written version to these proceedings. There were a number of other joint events including a banquet and an excursion to Munster ] . The IFIP WG 1.4 Scholarship was awarded to Andra s Antos for his paper Lower bounds on the rate of convergence of nonparametric pattern recognition."

Invited Lectures
Theoretical Views of Boostingp. 1
Open Theoretical Questions in Reinforcement Learningp. 11
Learning from Random Examples
A Geometric Approach to Leveraging Weak Learnersp. 18
Query by Committee, Linear Separation and Random Walksp. 34
Hardness Results for Neural Network Approximation Problemsp. 50
Learning from Queries and Counterexamples
Learnability of Quantified Formulasp. 63
Learning Multiplicity Automata from Smallest Counterexamplesp. 79
Exact Learning when Irrelevant Variables Aboundp. 91
An Application of Codes to Attribute-Efficient Learningp. 101
Learning Range Restricted Horn Expressionsp. 111
Reinforcement Learning
On the Asymptotic Behavior of a Constant Stepsize Temporal-Difference Learning Algorithmp. 126
On-line Learning and Expert Advice
Direct and Indirect Algorithms for On-line Learning of Disjunctionsp. 138
Averaging Expert Predictionsp. 153
Teaching and Learning
On Teaching and Learning Intersection-Closed Concept Classesp. 168
Inductive Inference
Avoiding Coding Tricks by Hyperrobust Learningp. 183
Mind Change Complexity of Learning Logic Programsp. 198
Statistical Theory of Learning and Pattern Recognition
Regularized Principal Manifoldsp. 214
Distribution-Dependent Vapnik-Chervonenkis Boundsp. 230
Lower Bounds on the Rate of Convergence of Nonparametric Pattern Recognitionp. 241
On Error Estimation for the Partitioning Classification Rulep. 253
Margin Distribution Bounds on Generalizationp. 263
Generalization Performance of Classifiers in Terms of Observed Covering Numbersp. 274
Entropy Numbers, Operators and Support Vector Kernelsp. 285
Author Indexp. 301
Table of Contents provided by Publisher. All Rights Reserved.

ISBN: 9783540657019
ISBN-10: 3540657010
Series: Lecture Notes in Computer Science,
Audience: General
Format: Paperback
Language: English
Number Of Pages: 299
Published: 17th March 1999
Publisher: Springer-Verlag Berlin and Heidelberg Gmbh & Co. Kg
Country of Publication: DE
Dimensions (cm): 23.39 x 15.6  x 1.73
Weight (kg): 0.45