Kernel based algorithms for mining huge data sets : supervised, semi-supervised, and unsupervised learning
著者
書誌事項
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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注記
Includes bibliographical references (p. 247-255) and indexes
内容説明・目次
内容説明
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.
目次
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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