MATLAB & Simulink Based Books

Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning

		Vojislav Kecman, University of Auckland Te-Ming Huang, The University of Auckland Ivica Kopriva   Springer,  2006 Tel: 212-460-1500 Fax: 212-460-1575 Email: service-ny@springer.com Outside North America Tel: +49 30 827 87 5431 Fax: +49 30 827 87 5707 ISBN: 3-540-31681-7 Language: English

Written for engineers and scientists, this book provides an introduction to the theory and algorithms for mining huge data sets. Topics covered include manifold approaches, component analysis, and low density separation.

MATLAB is introduced and used to solve some examples in the book. In addition, a companion set of MATLAB M-files is available for download.

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