Kernel Based Algorithms for Mining Huge Data Sets
Supervised, Semi-supervised, and Unsupervised Learning
(Sprache: Englisch)
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...
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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.
Inhaltsverzeichnis zu „Kernel Based Algorithms for Mining Huge Data Sets “
- 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.
Bibliographische Angaben
- Autoren: Te-Ming Huang , Vojislav Kecman , Ivica Kopriva
- 2006, 2006, 260 Seiten, Maße: 16 x 24,1 cm, Gebunden, Englisch
- Verlag: Springer
- ISBN-10: 3540316817
- ISBN-13: 9783540316817
- Erscheinungsdatum: 02.03.2006
Sprache:
Englisch
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