ISBN-10:
3540316817
ISBN-13:
9783540316817
Pub. Date:
04/13/2006
Publisher:
Springer Berlin Heidelberg
Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning / Edition 1

Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning / Edition 1

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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.

Product Details

ISBN-13: 9783540316817
Publisher: Springer Berlin Heidelberg
Publication date: 04/13/2006
Series: Studies in Computational Intelligence , #17
Edition description: 2006
Pages: 260
Product dimensions: 6.10(w) x 9.25(h) x 0.36(d)

Table of Contents

Introduction.- 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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