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) - Grāmatas - Springer-Verlag Berlin and Heidelberg Gm - 9783642068560 - 2010. gada 25. novembris
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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

Mediji Grāmatas     Paperback Book   (Grāmata ar mīksto vāku un līmēto muguru)
Izlaists 2010. gada 25. novembris
ISBN13 9783642068560
Izdevēji Springer-Verlag Berlin and Heidelberg Gm
Lapas 260
Izmēri 156 × 234 × 14 mm   ·   394 g
Valoda Angļu  

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