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Machine Learning, Data Warehousing & Mining, Mathematical Equations - Integral
Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-Supervised, and Unsupervised Learning by Huang, Te-Ming , Kecman, Vojislav , Kopriva, Ivica — book cover

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

by Huang, Te-Ming, Kecman, Vojislav, Kopriva, Ivica
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Overview

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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Book Details

Published
November 23, 2010
Publisher
Springer-Verlag New York, LLC
Pages
276
Format
Paperback
ISBN
9783642068560

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