Kernel Based Algorithms for Mining Huge Data Sets : Supervised, Semi-supervised, And Unsupervised Learning

Huang, Te-ming; Kecman, Vojislav; Kopriva, Ivica

ISBN 10: 3540316817 / ISBN 13: 9783540316817

Edité par Springer, 2006

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Détails bibliographiques

• Titre: Kernel Based Algorithms for Mining Huge Data Sets : Supervised, Semi-supervised, And Unsupervised Learning
• Éditeur: Springer
• Date de parution: 2006
• Langue: anglais
• ISBN à 10 chiffres: 3540316817
• ISBN à 13 chiffres: 9783540316817
• Reliure: Couverture rigide
• État de l'article: As New

A propos de ce titre

Synopsis

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.