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Learning with kernels : support vector machines, regularization, optimization, and beyond
In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs -kernels, for a number of learning tasks. Ker...
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| Auteurs principaux : | , |
|---|---|
| Format : | Livre |
| Langue : | anglais |
| Titre complet : | Learning with kernels : support vector machines, regularization, optimization, and beyond / Bernhard Schölkopf, Alexander J. Smola |
| Publié : |
Cambridge (Mass.), London :
The MIT Press
, C 2002 |
| Description matérielle : | 1 vol. (XVIII-626 p.) |
| Collection : | Adaptative computation and machine learning series |
| Sujets : |
Bib. CRDM (Mathématiques)
| | Cote | Prêt | Statut |
|---|---|---|---|
| Bibliothèque | 68C478 | Prêt sans prolongation | Disponible |