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Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning - Studies in Computational Intelligence 1997 edition
Huang, Te-ming (The University of Auckland)
Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning - Studies in Computational Intelligence 1997 edition
Huang, Te-ming (The University of Auckland)
Presents both the theory and the algorithms for mining huge data sets using support vector machines (SVMs) in an iterative way. This book 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
Media | Książki Hardcover Book (Książka z twardym grzbietem i okładką) |
Wydane | 2 marca 2006 |
ISBN13 | 9783540316817 |
Wydawcy | Springer-Verlag Berlin and Heidelberg Gm |
Strony | 260 |
Wymiary | 156 × 234 × 17 mm · 576 g |
Język | English |