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

Mining The Web Discovering Knowledge From Hypertext Data 1st Edition Soumen Chakrabarti

  • SKU: BELL-2467804
Mining The Web Discovering Knowledge From Hypertext Data 1st Edition Soumen Chakrabarti
$ 31.00 $ 45.00 (-31%)

4.0

6 reviews

Mining The Web Discovering Knowledge From Hypertext Data 1st Edition Soumen Chakrabarti instant download after payment.

Publisher: Morgan Kaufmann
File Extension: PDF
File size: 23.81 MB
Pages: 344
Author: Soumen Chakrabarti
ISBN: 9781558607545, 1558607544
Language: English
Year: 2002
Edition: 1

Product desciption

Mining The Web Discovering Knowledge From Hypertext Data 1st Edition Soumen Chakrabarti by Soumen Chakrabarti 9781558607545, 1558607544 instant download after payment.

Mining the Web: Discovering Knowledge from Hypertext Data is the first book devoted entirely to techniques for producing knowledge from the vast body of unstructured Web data. Building on an initial survey of infrastructural issues — including Web crawling and indexing — Chakrabarti examines low-level machine learning techniques as they relate specifically to the challenges of Web mining. He then devotes the final part of the book to applications that unite infrastructure and analysis to bring machine learning to bear on systematically acquired and stored data. Here the focus is on results: the strengths and weaknesses of these applications, along with their potential as foundations for further progress. From Chakrabarti's work — painstaking, critical, and forward-looking — readers will gain the theoretical and practical understanding they need to contribute to the Web mining effort.

Related Products