Computational Learning Theory

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

Paul Fischer, Hans U. Simon (Herausgeber)

Buch | Softcover

X, 299 Seiten

1999
Springer Berlin (Verlag)
978-3-540-65701-9 (ISBN)

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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 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.

Erscheint lt. Verlag	17.3.1999
Reihe/Serie	Lecture Notes in Artificial Intelligence
Reihe/Serie	Lecture Notes in Computer Science
Zusatzinfo	X, 299 p.
Verlagsort	Berlin
Sprache	englisch
Maße	155 x 235 mm
Gewicht	416 g
Themenwelt	Informatik ► Theorie / Studium ► Algorithmen
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Schlagworte	Algorithm analysis and problem complexity • Algorithmic Learning • Computational Learning • Hardcover, Softcover / Informatik, EDV/Informatik • HC/Informatik, EDV/Informatik • Inductive Inference • Künstliche Intelligenz • learning • Learning theory • Maschinelles Lernen • Online Learning • pattern recognition • Reinforcement Learning
ISBN-10	3-540-65701-0 / 3540657010
ISBN-13	978-3-540-65701-9 / 9783540657019
Zustand	Neuware