Enhancing Kernel Methods for Pattern Classification

404.00

Get it by 10 Aug

Order in 16h6m

Explore other bestsellers in
Database Design & Programming

Secure Payments

Product Overview

ISBN 13	9783639182606
Author	Ke Tang
Book Description	Kernel methods are a new family of techniques with sound theoretical grounds. They have been shown to be powerful approaches to pattern classification problems. However, many of the newly created kernel methods are far from perfect, and extensions and improvements are always required to make them even more effective. This book investigates one important class of the kernel methods, the least square support vector machines (LS-SVM), and enhances its performance extensively. In particular, the LS-SVM is enhanced in the contexts of four sub-problems related to solving the pattern classification problem. That is, model selection, feature selection, building sparse kernel classifier and kernel classifier ensemble. The LS-SVM can be regarded as a representative of many other kernel methods, and thus many ideas presented in this book can be easily extended to enhance performance of those related kernel methods. The results obtained should be useful to professionals that work on the theoretical aspects of kernel methods, or anyone else who may be considering ustilizing kernel methods for real-world pattern classification problems.
Number of Pages	156 pages

Cart Total  404.00

Enhancing Kernel Methods for Pattern Classification

404.00