Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning
Author:   Te-Ming Huang ,  Vojislav Kecman ,  Ivica Kopriva
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Edition:   Softcover reprint of hardcover 1st ed. 2006
Volume:   17
ISBN:  

9783642068560


Pages:   260
Publication Date:   25 November 2010
Format:   Paperback
Availability:   Out of print, replaced by POD   Availability explained
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Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning


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Author:   Te-Ming Huang ,  Vojislav Kecman ,  Ivica Kopriva
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition:   Softcover reprint of hardcover 1st ed. 2006
Volume:   17
Dimensions:   Width: 15.50cm , Height: 1.40cm , Length: 23.50cm
Weight:   0.724kg
ISBN:  

9783642068560


ISBN 10:   3642068561
Pages:   260
Publication Date:   25 November 2010
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Out of print, replaced by POD   Availability explained
We will order this item for you from a manufatured on demand supplier.

Table of Contents

Support Vector Machines in Classification and Regression — An Introduction.- Iterative Single Data Algorithm for Kernel Machines from Huge Data Sets: Theory and Performance.- Feature Reduction with Support Vector Machines and Application in DNA Microarray Analysis.- Semi-supervised Learning and Applications.- Unsupervised Learning by Principal and Independent Component Analysis.

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