Titre: Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning (Studies in Computational Intelligence, 17)
�diteur: Springer
Date de parution: 2006
Langue: anglais
ISBN � 10 chiffres: 3540316817
ISBN � 13 chiffres: 9783540316817
Reliure: Couverture rigide
�tat de l'article: 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.

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