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

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

Yazar : Te-Ming Huang Vojislav Kecman Ivica Kopriva
Yayınevi : 
ISBN : 9783540316817
Baskı yılı : 2006
Sayfa sayısı : 260
Stok durumu : Tükendi   
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.
1 Introduction 1
2 Support vector machines in classification and regression - an introduction 11
3 Iterative single data algorithm for kernel machines from huge data sets : theory and performance 61
4 Feature reduction with support vector machines and application in DNA microarray analysis 97
5 Semi-supervised learning and applications 125
6 Unsupervised learning by principal and independent component analysis 175
A Support vector machines 209
B Matlab code for ISDA classification 217
C Matlab code for ISDA regression 223
D Matlab code for conjugate gradient method with box constraints 229
E Uncorrelatedness and independence 233
F Independent component analysis by empirical estimation of score functions i.e., probability density functions 237
G SemiL user guide 241