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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.
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Informazioni sull'autore
Santanu 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 Institute of Technology, Kolkata. His principal research interests include signal processing, machine learning and bioinformatics.
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Advances in Proximal Kernel Classifiers
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LAP LAMBERT Academic Publishing Nov 2012, 2012
ISBN 10: 365927836X
ISBN 13: 9783659278365
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Taschenbuch. Condizione: 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. Codice articolo 9783659278365
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Advances in Proximal Kernel Classifiers
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LAP LAMBERT Academic Publishing, 2012
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ISBN 13: 9783659278365
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Condizione: 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. Codice articolo 5145199
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Advances in Proximal Kernel Classifiers : Proximal Kernel Classifiers and its Application with MATLAB
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LAP LAMBERT Academic Publishing, 2012
ISBN 10: 365927836X
ISBN 13: 9783659278365
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Taschenbuch. Condizione: 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. Codice articolo 9783659278365
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Advances in Proximal Kernel Classifiers
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Advances in Proximal Kernel Classifiers
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ISBN 10: 365927836X
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Advances in Proximal Kernel Classifiers
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VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2012
ISBN 10: 365927836X
ISBN 13: 9783659278365
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Condizione: New. PRINT ON DEMAND pp. 244. Codice articolo 18128819893
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Advances in Proximal Kernel Classifiers: Proximal Kernel Classifiers and its Application with MATLAB
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LAP LAMBERT Academic Publishing, 2012
ISBN 10: 365927836X
ISBN 13: 9783659278365
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Advances in Proximal Kernel Classifiers
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LAP LAMBERT Academic Publishing Nov 2012, 2012
ISBN 10: 365927836X
ISBN 13: 9783659278365
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. 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.OmniScriptum SRL, Str. Armeneasca 28/1, office 1, 2012 Chisinau 244 pp. Englisch. Codice articolo 9783659278365
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Advances in Proximal Kernel Classifiers | Proximal Kernel Classifiers and its Application with MATLAB
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ISBN 13: 9783659278365
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Taschenbuch. Condizione: Neu. Advances in Proximal Kernel Classifiers | Proximal Kernel Classifiers and its Application with MATLAB | Santanu Ghorai (u. a.) | Taschenbuch | 244 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783659278365 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Codice articolo 106186704
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