Kernel Based Algorithms for Mining Huge Data Sets | Supervised, Semi-supervised, and Unsupervised Learning
Language: English
Published by Springer, 2010
- Softcover
- New
Condition: New
US$ 111.26
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Kernel Based Algorithms for Mining Huge Data Sets | Supervised, Semi-supervised, and Unsupervised Learning | Te-Ming Huang (u. a.) | Taschenbuch | Studies in Computational Intelligence | xvi | Englisch | 2010 | Springer | EAN 9783642068560 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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- Title
- Kernel Based Algorithms for Mining Huge Data Sets | Supervised, Semi-supervised, and Unsupervised Learning
- Author
- Te-Ming Huang (u. a.)
- Publisher
- Springer
- Publication year
- 2010
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 3642068561
- ISBN 13
- 9783642068560
- Item weight
- 724 grams
- Dimensions
- 297 x 210 x 16 mm
- Seller catalogs
- Bücher
"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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
"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.
"About the title" may belong to another edition of this title.
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