Kernel Based Algorithms for Mining Huge Data Sets (original) (raw)
Overview
Authors:
- Te-Ming Huang
- Faculty of Engineering, The University of Auckland, Auckland, New Zealand
- Vojislav Kecman
- Faculty of Engineering, The University of Auckland, Auckland, New Zealand
- Ivica Kopriva
- Department of Electrical and Computer Engineering, Washington D.C., USA
Reports recent research results on Kernel Based Algorithms for Mining Huge Data Sets
A book about (machine) learning from (experimental) data
Includes supplementary material: sn.pub/extras
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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.
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Table of contents (6 chapters)
Authors and Affiliations
Faculty of Engineering, The University of Auckland, Auckland, New Zealand
Te-Ming Huang, Vojislav Kecman
Department of Electrical and Computer Engineering, Washington D.C., USA
Ivica Kopriva
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Bibliographic Information
- Book Title: Kernel Based Algorithms for Mining Huge Data Sets
- Book Subtitle: Supervised, Semi-supervised, and Unsupervised Learning
- Authors: Te-Ming Huang, Vojislav Kecman, Ivica Kopriva
- Series Title: Studies in Computational Intelligence
- DOI: https://doi.org/10.1007/3-540-31689-2
- Publisher: Springer Berlin, Heidelberg
- eBook Packages: Engineering, Engineering (R0)
- Copyright Information: Springer-Verlag Berlin Heidelberg 2006
- Hardcover ISBN: 978-3-540-31681-7Published: 02 March 2006
- Softcover ISBN: 978-3-642-06856-0Published: 25 November 2010
- eBook ISBN: 978-3-540-31689-3Published: 21 May 2006
- Series ISSN: 1860-949X
- Series E-ISSN: 1860-9503
- Edition Number: 1
- Number of Pages: XVI, 260
- Topics: Data Mining and Knowledge Discovery, Mathematical and Computational Engineering, Artificial Intelligence