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.
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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.
Imprint | Springer-Verlag |
Country of origin | Germany |
Series | Studies in Computational Intelligence, 17 |
Release date | November 2010 |
Availability | Expected to ship within 10 - 15 working days |
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 |
Barcode | 9783642068560 |
Categories | |
LSN | 3-642-06856-1 |