logo

EbookBell.com

Most ebook files are in PDF format, so you can easily read them using various software such as Foxit Reader or directly on the Google Chrome browser.
Some ebook files are released by publishers in other formats such as .awz, .mobi, .epub, .fb2, etc. You may need to install specific software to read these formats on mobile/PC, such as Calibre.

Please read the tutorial at this link:  https://ebookbell.com/faq 


We offer FREE conversion to the popular formats you request; however, this may take some time. Therefore, right after payment, please email us, and we will try to provide the service as quickly as possible.


For some exceptional file formats or broken links (if any), please refrain from opening any disputes. Instead, email us first, and we will try to assist within a maximum of 6 hours.

EbookBell Team

Kernel Based Algorithms For Mining Huge Data Sets Supervised Semisupervised And Unsupervised Learning 1st Edition Teming Huang

  • SKU: BELL-1633574
Kernel Based Algorithms For Mining Huge Data Sets Supervised Semisupervised And Unsupervised Learning 1st Edition Teming Huang
$ 31.00 $ 45.00 (-31%)

4.7

96 reviews

Kernel Based Algorithms For Mining Huge Data Sets Supervised Semisupervised And Unsupervised Learning 1st Edition Teming Huang instant download after payment.

Publisher: Springer
File Extension: PDF
File size: 4.64 MB
Pages: 276
Author: Te-Ming Huang, Vojislav Kecman, Ivica Kopriva
ISBN: 9783540316817, 9783540316893, 3540316817, 3540316892
Language: English
Year: 2006
Edition: 1

Product desciption

Kernel Based Algorithms For Mining Huge Data Sets Supervised Semisupervised And Unsupervised Learning 1st Edition Teming Huang by Te-ming Huang, Vojislav Kecman, Ivica Kopriva 9783540316817, 9783540316893, 3540316817, 3540316892 instant download after payment.

This 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 using support vector machines (SVMs) in an iterative way. It demonstrates how kernel based SVMs can be used for dimensionality reduction and shows the similarities and differences between the two most popular unsupervised techniques.

Related Products