Support vector machines optimization based theory, algorithms, and extensions
"Preface Support vector machines (SVMs), which were introduced by Vapnik in the early 1990s, are proved effective and promising techniques for data mining. SVMs have recently been breakthroughs in advance in their theoretical studies and implementations of algorithms. They have been successfull...
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| Otros Autores: | , |
| Formato: | Libro electrónico |
| Idioma: | Inglés |
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Boca Raton :
CRC Press, Taylor & Francis Group
2013.
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| Edición: | 1st edition |
| Colección: | Chapman & Hall/CRC data mining and knowledge discovery series.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009628516306719 |
Tabla de Contenidos:
- Front Cover; Dedication; Contents; List of Figures; List of Tables; Preface; List of Symbols; 1. Optimization; 2. Linear Classification; 3. Linear Regression; 4. Kernels and Support Vector Machines; 5. Basic Statistical Learning Theory of C-Support Vector Classification; 6. Model Construction; 7. Implementation; 8. Variants and Extensions of Support Vector Machines; Bibliography
