Advances in Learning Theory: Methods, Models and Applications

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Название: Advances in Learning Theory: Methods, Models and Applications

Автор: Johan Suykens , Gabor Horvath

Издательство: IOS Press, Incorporated

Год: 2003

Страниц: 432

Формат: pdf

Размер: 3 Mб

Язык: английский

New methods, models, and applications in learning theory were the central themes of a NATO Advanced Study Institute held in July 2002. Contributors in neural networks, machine learning, mathematics, statistics, signal processing, and systems and control shed light on areas such as regularization parameters in learning theory, Cucker Smale learning theory in Besov spaces, high-dimensional approximation by neural networks, and functional learning through kernels. Other subjects discussed include leave-one-out error and stability of learning algorithms with applications, regularized least-squares classification, support vector machines, kernels methods for text processing, multiclass learning with output codes, Bayesian regression and classification, and nonparametric prediction.