Enhancing Kernel Methods for Pattern Classification : Theories and Implementations
Language: English
Published by VDM Verlag Dr. Müller, 2009
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 54.78
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Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - 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, andextensions 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.…
Seller Inventory # 9783639182606
- Title
- Enhancing Kernel Methods for Pattern Classification : Theories and Implementations
- Author
- Ke Tang
- Publisher
- VDM Verlag Dr. Müller
- Publication year
- 2009
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 363918260X
- ISBN 13
- 9783639182606
- Item weight
- 225 grams
- Dimensions
- 220x150x10 mm
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