Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning (Studies in Computational Intelligence (17))

Huang, Te-Ming, Kecman, Vojislav, Kopriva, Ivica

ISBN 10: 3540316817 / ISBN 13: 9783540316817

Edité par Springer, 2006

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N° de réf. du vendeur ERICA75835403168175

Détails bibliographiques

• Titre: Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning (Studies in Computational Intelligence (17))
• Éditeur: Springer
• Date de parution: 2006
• Langue: anglais
• ISBN à 10 chiffres: 3540316817
• ISBN à 13 chiffres: 9783540316817
• Reliure: Hardcover
• État de l'article: Like New
• Type de livre: book

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.