advances proximal kernel classifiers de ghorai santanu (8 résultats)

Etat : New. pp. 244.

Taschenbuch. Etat : Neu. Neuware -The book describes the development and performance of proximal classifiers, a class of kernel-based regularized mean square error type classifier that learns within the penalized modeling paradigm. The name proximal classifier indicates the fact of classification of a test pattern by its proximity either to a hyperplane or to a class centroid. The basic idea of the nonparallel plane classifier is to model each class of data by fitting separate hyperplane through it. A computationally efficient binary Nonparallel Plane Proximal Classifier (NPPC) is described in detail along with its nonlinear extension. NPPC is also extended to classify multiclass data. A new approach of multiclass data classification through vector-valued regression technique by the proximity to a class centroid is described in detail. These classifiers are applied to discriminate cancerous tissue samples from gene microarray data. The book provides a complete literature survey in the field of Support Vector Machine (SVM). It includes mathematical models, detailed solution procedures and algorithms of the different proximal classifiers with hands-on examples and well-documented MATLAB programs.Books on Demand GmbH, Überseering 33, 22297 Hamburg 244 pp. Englisch.

Paperback. Etat : Like New. Like New. book.

Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The book describes the development and performance of proximal classifiers, a class of kernel-based regularized mean square error type classifier that learns within the penalized modeling paradigm. The name proximal classifier indicates the fact of classification of a test pattern by its proximity either to a hyperplane or to a class centroid. The basic idea of the nonparallel plane classifier is to model each class of data by fitting separate hyperplane through it. A computationally efficient binary Nonparallel Plane Proximal Classifier (NPPC) is described in detail along with its nonlinear extension. NPPC is also extended to classify multiclass data. A new approach of multiclass data classification through vector-valued regression technique by the proximity to a class centroid is described in detail. These classifiers are applied to discriminate cancerous tissue samples from gene microarray data. The book provides a complete literature survey in the field of Support Vector Machine (SVM). It includes mathematical models, detailed solution procedures and algorithms of the different proximal classifiers with hands-on examples and well-documented MATLAB programs. 244 pp. Englisch.

Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ghorai SantanuSantanu Ghorai received the ME degree in electrical engineering from the Jadavpur University in 2000 and the PhD degree from the Indian Institute of Technology, Kharagpur, in 2011. Currently he is with the Heritage Inst.

Etat : New. Print on Demand pp. 244 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Etat : New. PRINT ON DEMAND pp. 244.

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The book describes the development and performance of proximal classifiers, a class of kernel-based regularized mean square error type classifier that learns within the penalized modeling paradigm. The name proximal classifier indicates the fact of classification of a test pattern by its proximity either to a hyperplane or to a class centroid. The basic idea of the nonparallel plane classifier is to model each class of data by fitting separate hyperplane through it. A computationally efficient binary Nonparallel Plane Proximal Classifier (NPPC) is described in detail along with its nonlinear extension. NPPC is also extended to classify multiclass data. A new approach of multiclass data classification through vector-valued regression technique by the proximity to a class centroid is described in detail. These classifiers are applied to discriminate cancerous tissue samples from gene microarray data. The book provides a complete literature survey in the field of Support Vector Machine (SVM). It includes mathematical models, detailed solution procedures and algorithms of the different proximal classifiers with hands-on examples and well-documented MATLAB programs.