- Series: Studies in Computational Intelligence (Book 17)
- Hardcover: 260 pages
- Publisher: Springer; 2006 edition (April 13, 2006)
- Language: English
- ISBN-10: 3540316817
- ISBN-13: 978-3540316817
- Product Dimensions: 6.1 x 0.7 x 9.2 inches
- Shipping Weight: 1.2 pounds (View shipping rates and policies)
- Average Customer Review: 1 customer review
- Amazon Best Sellers Rank: #6,516,125 in Books (See Top 100 in Books)
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Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning (Studies in Computational Intelligence) 2006th Edition
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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.
Top customer reviews
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The authors tend to include in-line notes several times per paragraph. There was some decent information included (examples or related words), but overall it was too distracting (the notes in parentheses trick was overused) and I had to look for another book (because this was maddening).