Least Squares Support Vector Machines.

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This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors expla...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Suykens, Johan A. K.
Otros Autores: Gestel, Tony van, De Brabanter, Jos, De Moor, Bart, Vandewalle, Joos
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Singapore : World Scientific Publishing Company, 2002.
Temas:
Algorithms.
Kernel functions.
Least squares.
Machine learning.
Algorithms
Machine Learning
Algorithmes.
Noyaux (Mathématiques)
Apprentissage automatique.
algorithms.
Kernel functions
Least squares
Machine learning
Acceso en línea:Texto completo
Descripción
Sumario:This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing sparseness and employing robust statistics. The framework is further extended towards unsupervised learning by considering PCA.
Descripción Física:1 online resource (308 pages)
Bibliografía:Includes bibliographical references (pages 269-286) and index.
ISBN:9789812776655
9812776656

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