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Least squares support vector machines /
Annotation.
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
River Edge, NJ :
World Scientific,
2002.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 0 | 0 | |a Least squares support vector machines / |c Johan A.K. Suykens [and others]. |
| 260 | |a River Edge, NJ : |b World Scientific, |c 2002. | ||
| 300 | |a 1 online resource (xiv, 294 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 520 | 8 | |a Annotation. |b 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 spareness and employing robust statistics. The framework is further extended towards unsupervised learning by considering PCA analysis and its kernel version as a one-class modelling problem. This leads to new primal-dual support vector machine formulations for kernel PCA and kernel CCA analysis. Furthermore, LS-SVM formulations are given for recurrent networks and control. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, a method of fixed size LS-SVM is proposed where the estimation is done in the primal space in relation to a Nystrom sampling with active selection of support vectors. The methods are illustrated with several examples. | |
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Algorithms. | |
| 650 | 0 | |a Kernel functions. | |
| 650 | 0 | |a Least squares. | |
| 650 | 2 | |a Algorithms | |
| 650 | 2 | |a Least-Squares Analysis | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Algorithmes. | |
| 650 | 6 | |a Noyaux (Mathématiques) | |
| 650 | 6 | |a Moindres carrés. | |
| 650 | 7 | |a algorithms. |2 aat | |
| 650 | 7 | |a COMPUTERS |x Enterprise Applications |x Business Intelligence Tools. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Intelligence (AI) & Semantics. |2 bisacsh | |
| 650 | 7 | |a Algorithms. |2 fast |0 (OCoLC)fst00805020 | |
| 650 | 7 | |a Kernel functions. |2 fast |0 (OCoLC)fst00986892 | |
| 650 | 7 | |a Least squares. |2 fast |0 (OCoLC)fst00995082 | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 700 | 1 | |a Suykens, Johan A. K. | |
| 776 | 0 | 8 | |i Print version: |t Least squares support vector machines. |d River Edge, NJ : World Scientific, 2002 |w (DLC) 2002033063 |
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