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Kernel Based Algorithms for Mining Huge Data Sets - Supervised, Semi-supervised, and Unsupervised Learning (Paperback, Softcover reprint of hardcover 1st ed. 2006) Loot Price: R2,702
Discovery Miles 27 020
Repayment Terms: R253 pm x 12*
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Kernel Based Algorithms for Mining Huge Data Sets - Supervised, Semi-supervised, and Unsupervised Learning (Paperback,...

Kernel Based Algorithms for Mining Huge Data Sets - Supervised, Semi-supervised, and Unsupervised Learning (Paperback, Softcover reprint of hardcover 1st ed. 2006)

Te-Ming Huang, Vojislav Kecman, Ivica Kopriva

Series: Studies in Computational Intelligence, 17

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Loot Price R2,702 Discovery Miles 27 020 | Repayment Terms: R253 pm x 12*

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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.

General

Imprint: Springer-Verlag
Country of origin: Germany
Series: Studies in Computational Intelligence, 17
Release date: November 2010
First published: 2006
Authors: Te-Ming Huang • Vojislav Kecman • Ivica Kopriva
Dimensions: 235 x 155 x 14mm (L x W x T)
Format: Paperback
Pages: 260
Edition: Softcover reprint of hardcover 1st ed. 2006
ISBN-13: 978-3-642-06856-0
Categories: Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematics for scientists & engineers
Books > Computing & IT > Applications of computing > Databases > Data mining
Books > Computing & IT > Applications of computing > Artificial intelligence > General
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LSN: 3-642-06856-1
Barcode: 9783642068560

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