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020 a| 9789812381514;

020 a| 9812381511 (Trade Cloth)c| USD 88.00 Retail Price (Publisher)9| Active Record;

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100 1 a| Suykens, Johan A. K.e| Author=| ^A870858;

245 10 a| Least Squares Support Vector Machinesh| [electronic resource];

260 a| Hackensack : b| World Scientific Publishing Company, Incorporated;

300 a| 308 p.;

506 a| Available only to authorized users.;

520 8 a| Annotationb| 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.;

538 a| Mode of access: World Wide Web;

650 4 a| Least Squares;

655 0 a| Electronic books.=| ^A491897;

700 1 a| Van Gestel, Tonye| Author?| UNAUTHORIZED;

700 1 a| De Brabanter, Jose| Author?| UNAUTHORIZED;

700 1 a| De Moor, Barte| Author?| UNAUTHORIZED;

700 1 a| Vandewalle, Joose| Author?| UNAUTHORIZED;

856 40 z| Full text available from Ebook Central - Academic Completeu| https://ebookcentral.proquest.com/lib/eastcarolina/detail.action?docID=1681619;

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Least Squares Support Vector Machines

Author/creator	Suykens, Johan A. K. Author
Format	Electronic
Publication Info	Hackensack : World Scientific Publishing Company, Incorporated
Description	308 p.
Supplemental Content	Full text available from Ebook Central - Academic Complete
Subjects	Least Squares

Other author/creator	Van Gestel, Tony Author
Other author/creator	De Brabanter, Jos Author
Other author/creator	De Moor, Bart Author
Other author/creator	Vandewalle, Joos Author
Summary	Annotation 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.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Genre/form	Electronic books.
ISBN	9789812381514
ISBN	9812381511 (Trade Cloth) Active Record
Standard identifier#	9789812381514
Stock number	00041155

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