Kernel Based Algorithms for Mining Huge Data Sets - Supervised, Semi-supervised, and Unsupervised Learning (Paperback, Softcover reprint of hardcover 1st ed. 2006)

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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.


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Product Description

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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Product Details

General

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

, ,

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



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