Overview
- 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
Part of the book series: Studies in Computational Intelligence (SCI, volume 17)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (6 chapters)
-
Front Matter
-
Back Matter
Keywords
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.
Authors and Affiliations
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