Kernel Based Algorithms for Mining Huge Data Sets

Supervised, Semi-supervised, and Unsupervised Learning

  • Te-Ming Huang
  • Vojislav Kecman
  • Ivica Kopriva

Part of the Studies in Computational Intelligence book series (SCI, volume 17)

About this book


"Kernel Based Algorithms for Mining Huge Data Sets" 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 by using support vector machines (SVMs) in an iterative way. It demonstrates how kernel based SVMs can be used for dimensionality reduction (feature elimination) and shows the similarities and differences between the two most popular unsupervised techniques, the principal component analysis (PCA) and the independent component analysis (ICA). The book presents various examples, software, algorithmic solutions enabling the reader to develop their own codes for solving the problems. The book is accompanied by a website for downloading both data and software for huge data sets modeling in a supervised and semisupervised manner, as well as MATLAB based PCA and ICA routines for unsupervised learning. The book focuses on a broad range of machine learning algorithms and it is particularly aimed at students, scientists, and practicing researchers in bioinformatics (gene microarrays), text-categorization, numerals recognition, as well as in the images and audio signals de-mixing (blind source separation) areas.


Analysis MATLAB Regression Signal algorithm algorithms bioinformatics classification learning machine learning modeling unsupervised learning

Authors and affiliations

  • Te-Ming Huang
    • 1
  • Vojislav Kecman
    • 1
  • Ivica Kopriva
    • 2
  1. 1.Faculty of EngineeringThe University of AucklandAucklandNew Zealand
  2. 2.Department of Electrical and Computer EngineeringWashington D.C.USA

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2006
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-540-31681-7
  • Online ISBN 978-3-540-31689-3
  • Series Print ISSN 1860-949X
  • Buy this book on publisher's site