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Kernel Based Algorithms for Mining Huge Data Sets By Te-Ming Huang

Kernel Based Algorithms for Mining Huge Data Sets by Te-Ming Huang

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Summary

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.

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Kernel Based Algorithms for Mining Huge Data Sets Summary

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

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.

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.

Additional information

NLS9783642068560
9783642068560
3642068561
Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning by Te-Ming Huang
New
Paperback
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
2010-11-23
260
N/A
Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
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