Least Squares Support Vector Machines.
This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors expla...
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| Online Access: |
Full text (MCPHS users only) |
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| Main Author: | |
| Other Authors: | , , , |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Singapore :
World Scientific Publishing Company,
2002
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| Subjects: | |
| Local Note: | ProQuest Ebook Central |
| Summary: | This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing sparseness and employing robust statistics. The framework is further extended towards unsupervised learning by considering PCA. |
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| Physical Description: | 1 online resource (308 pages) |
| Bibliography: | Includes bibliographical references (pages 269-286) and index. |
| ISBN: | 9789812776655 9812776656 |
| Source of Description, Etc. Note: | Print version record. |
