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".
Inhaltsverzeichnis
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.
