Kernel based algorithms for mining huge data sets : supervised, semi-supervised, and unsupervised learning

Te-Ming Huang, Vojislav Kecman, Ivica Kopriva

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.

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[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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この本の情報

書名 Kernel based algorithms for mining huge data sets : supervised, semi-supervised, and unsupervised learning
著作者等 Kecman, V.
Huang Te-Ming (The University of Auckland)
Kecman Vojislav
Kopriva Ivica
Huang Te-Ming
シリーズ名 Studies in computational intelligence
出版元 Springer
刊行年月 c2006
ページ数 xvi, 260 p.
大きさ 24 cm
ISBN 3540316817
NCID BA77316463
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言語 英語
出版国 ドイツ
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