Title
Advances in Proximal Kernel Classifiers: Proximal Kernel Classifiers and its Application with MATLAB,Used
Delivery time: 8-12 business days (International)
The book describes the development and performance of proximal classifiers, a class of kernelbased 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 vectorvalued 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 handson examples and welldocumented MATLAB programs.
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This warranty strictly does not cover damages that arose from negligence, misuse, wear and tear, or not in accordance with product instructions (dropping the product, etc.).
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