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Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning - Studies in Computational Intelligence Huang, Te-ming (The University of Auckland) 1st Ed. Softcover of Orig. Ed. 2006 edition
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También disponible como:
Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning - Studies in Computational Intelligence
Huang, Te-ming (The University of Auckland)
This is the first book treating the fields of supervised, semi-supervised and unsupervised machine learning collectively. The book presents both the theory and the algorithms for mining huge data sets using support vector machines (SVMs) in an iterative way. It demonstrates how kernel based SVMs can be used for dimensionality reduction and shows the similarities and differences between the two most popular unsupervised techniques.
260 pages, 19 black & white tables, biography
| Medios de comunicación | Libros Paperback Book (Libro con tapa blanda y lomo encolado) |
| Publicado | 25 de noviembre de 2010 |
| ISBN13 | 9783642068560 |
| Editores | Springer-Verlag Berlin and Heidelberg Gm |
| Páginas | 260 |
| Dimensiones | 156 × 234 × 14 mm · 394 g |
| Lengua | Inglés |