Te-Ming Huang,
Vojislav Kecman,
Ivica Kopriva
Springer Berlin Heidelberg
2006e druk, 2006
9783540316817
Kernel Based Algorithms for Mining Huge Data Sets
Supervised, Semi-supervised, and Unsupervised Learning
Specificaties
Gebonden, 260 blz.
|
Engels
Springer Berlin Heidelberg |
2006e druk, 2006
ISBN13: 9783540316817
Rubricering
Onderdeel van serie
Studies in Computational Intelligence
Verwachte levertijd ongeveer 9 werkdagen
Samenvatting
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.
Specificaties
ISBN13:9783540316817
Taal:Engels
Bindwijze:gebonden
Aantal pagina's:260
Uitgever:Springer Berlin Heidelberg
Druk:2006
Hoofdrubriek:Databases, Computer en informatica
Inhoudsopgave
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.