Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning - Studies in Computational Intelligence 17 (Paperback)Te-Ming Huang (author), Vojislav Kecman (author), Ivica Kopriva (author)
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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.
Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Number of pages: 260
Weight: 721 g
Dimensions: 235 x 155 x 14 mm
Edition: Softcover reprint of hardcover 1st ed. 2006
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