Advances in learning theory : methods, models and applications / edited by Johan Suykens [and others].

"Proceedings of the NATO Advanced Study Institute on Learning Theory and Practice, 8-19 July 2002, Leuven, Belgium"--Title page verso.

"Published in cooperation with NATO Scientific Affairs Division."

Includes bibliographical references and indexes.

Cover; Title page; Preface; Organizing committee; List of chapter contributors; Contents; 1 An Overview of Statistical Learning Theory; 2 Best Choices for Regularization Parameters in Learning Theory: On the Bias-Variance Problem; 3 Cucker Smale Learning Theory in Besov Spaces; 4 High-dimensional Approximation by Neural Networks; 5 Functional Learning through Kernels; 6 Leave-one-out Error and Stability of Learning Algorithms with Applications; 7 Regularized Least-Squares Classification; 8 Support Vector Machines: Least Squares Approaches and Extensions.

This text details advances in learning theory that relate to problems studied in neural networks, machine learning, mathematics and statistics.

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