Cargando…
Advances in large margin classifiers /
The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classi...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cambridge, Mass. :
MIT Press,
©2000.
|
| Colección: | Neural information processing series.
|
| Temas: | |
| Acceso en línea: | Texto completo |
| Sumario: | The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification--that is, a scale parameter--rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms. The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba. |
|---|---|
| Descripción Física: | 1 online resource (vi, 412 pages) : illustrations |
| Bibliografía: | Includes bibliographical references (pages 389-407) and index. |
| ISBN: | 9780262283977 0262283972 1423729544 9781423729549 0262292408 9780262292405 0262194481 9780262194488 |