Kernel Based Algorithms for Mining Huge Data Sets

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Te-Ming Huang, Vojislav Kecman, Ivica Kopriva

Kernel Based Algorithms for Mining Huge Data Sets

Name: Kernel Based Algorithms for Mining Huge Data Sets
Author: Te-Ming Huang

Supervised, Semi-supervised, and Unsupervised Learning

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Gebonden, 260 blz. | Engels

Springer Berlin Heidelberg | 2006e druk, 2006

ISBN13: 9783540316817

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Springer Berlin Heidelberg 2006e druk, 2006 9783540316817

Onderdeel van serie Studies in Computational Intelligence

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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.

Specificaties

ISBN13:9783540316817

Taal:Engels

Bindwijze:gebonden

Aantal pagina's:260

Uitgever:Springer Berlin Heidelberg

Druk:2006

Hoofdrubriek:Databases, Computer en informatica

Serie:Studies in Computational Intelligence

Inhoudsopgave

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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Kernel Based Algorithms for Mining Huge Data Sets

Supervised, Semi-supervised, and Unsupervised Learning

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