Kernel based algorithms for mining huge data sets : supervised, semi-supervised, and unsupervised learning
Author(s)
Bibliographic Information
Kernel based algorithms for mining huge data sets : supervised, semi-supervised, and unsupervised learning
（Studies in computational intelligence, v. 17）
Springer, c2006
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Note
Includes bibliographical references (p. 247-255) and indexes
Description and Table of Contents
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.
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.
by "Nielsen BookData"
