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) - Books - Springer-Verlag Berlin and Heidelberg Gm - 9783642068560 - November 25, 2010
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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 1st Ed. Softcover of Orig. Ed. 2006 edition

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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

Media Books     Paperback Book   (Book with soft cover and glued back)
Released November 25, 2010
ISBN13 9783642068560
Publishers Springer-Verlag Berlin and Heidelberg Gm
Pages 260
Dimensions 156 × 234 × 14 mm   ·   394 g
Language English  

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