Amazon cover image
Image from Amazon.com
Image from Google Jackets

Machine learning and data mining in pattern recognition : 6th international conference, MLDM 2009, Leipzig, Germany, July 23-25, 2009 : proceedings / Petra Perner (ed.).

By: Contributor(s): Material type: TextTextSeries: Lecture notes in computer science ; 5632 | Lecture notes in artifical intelligencePublication details: Berlin : Springer, 2009.Description: xiv, 824 p. : ill. ; 24 cmISBN:
  • 9783642030697 (pbk.)
  • 3642030696
Other title:
  • MLDM 2009
Subject(s): DDC classification:
  • 006.31   22
Summary: This book constitutes the refereed proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2009, held in Leipzig, Germany, in July 2009. The 63 revised full papers presented were carefully reviewed and selected from 205 submissions. The papers are organized in topical sections on attribute discretization and data preparation; classification; ensemble classifier learning; associate rules and pattern minig; support vector machines; clustering; novelty and outlier detection; learning; data mining and multimedia data; text mining; aspects of data mining; as well as data mining in medicine.
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
Holdings
Item type Current library Call number Copy number Status Date due Barcode
Books Books Main library General Stacks 006.31 / PE.M 2009 (Browse shelf(Opens below)) 1 Available 006285

International conference proceedings.

Includes bibliographical references and index.

This book constitutes the refereed proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2009, held in Leipzig, Germany, in July 2009. The 63 revised full papers presented were carefully reviewed and selected from 205 submissions. The papers are organized in topical sections on attribute discretization and data preparation; classification; ensemble classifier learning; associate rules and pattern minig; support vector machines; clustering; novelty and outlier detection; learning; data mining and multimedia data; text mining; aspects of data mining; as well as data mining in medicine.

1

There are no comments on this title.

to post a comment.