BIBLIO is the largest independent book marketplace in the world, with over 100 million books.

Skip to content

Machine Learning and Knowledge Discovery In Databases

Machine Learning and Knowledge Discovery In Databases

Machine Learning and Knowledge Discovery In Databases
Stock photo: cover may vary

Machine Learning and Knowledge Discovery In Databases Paperback - 2015

by ,

Add to wish list
  • New
New

Description

new.
Ask the seller a question Add to wish list
A$154.25
A$5.64 Delivery within USA
Standard delivery: 2 to 14 days
More delivery options
Ships from GreatBookPrices (Maryland, United States)

Details

  • Title Machine Learning and Knowledge Discovery In Databases
  • Author ,
  • Binding Paperback
  • Condition New
  • Publisher Springer
  • Publication date 2015
  • Features Illustrated
  • Bookseller's Inventory # 24373974-n
  • ISBN 9783319235240
  • Quantity available 5

About GreatBookPrices Maryland, United States

Biblio member since 2024

Since 1991, we have worked every day to serve our customers with state-of-the-art technology and world class service. We are dedicated to providing customers around the world with the widest selection of books, DVDs, and CDs at the absolute lowest price.

Terms of Sale: 30 day return guarantee, with full refund including original shipping costs for up to 30 days after delivery if an item arrives misdescribed or damaged.

Browse books from GreatBookPrices

Reader reviews for Machine Learning and Knowledge Discovery In Databases

From the publisher

The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, 17 demo papers. They were organized in topical sections named: classification, regression and supervised learning; clustering and unsupervised learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.
tracking-