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Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-Supervised, and Unsupervised Learning
Hardcover
Series: Studies in Computational Intelligence, Book 17
DatabasesGeneral ComputersGeneral Mathematics
ISBN13: 9783540316817
Publisher: Springer
Published: Mar 2 2006
Pages: 260
Weight: 1.27
Height: 0.69 Width: 6.14 Depth: 9.21
Language: English
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.
1 different editions
Also available
Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-Supervised, and Unsupervised Learning
Huang, Te-Ming
Kecman, Vojislav
Kopriva, Ivica
Paperback
Also from
Huang, Te-Ming
Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-Supervised, and Unsupervised Learning
Huang, Te-Ming
Kecman, Vojislav
Kopriva, Ivica
Paperback
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