Kernel Based Algorithms for Mining Huge Data Sets

Language: English

Published by Springer, Springer Nov 2010, 2010

3642068561 / 9783642068560

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This item is printed on demand - Print on Demand Titel. Neuware -'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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 276 pp. Englisch.…

Seller Inventory # 9783642068560

Title
Kernel Based Algorithms for Mining Huge Data Sets
Author
Te-Ming Huang
Publisher
Springer, Springer Nov 2010
Publication year
2010
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3642068561
ISBN 13
9783642068560
Item weight
724 grams
Dimensions
297x210x16 mm

buchversandmimpf2000

Emtmannsberg, BAYE, Germany

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