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Multiple Classifier Systems

Multiple Classifier Systems

Multiple Classifier Systems
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Multiple Classifier Systems Papeback -

by Michal Haindl (Editor); Josef Kittler (Editor); Fabio Roli (Editor)

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Springer , pp. 540 . Papeback. New.
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Details

  • Title Multiple Classifier Systems
  • Author Michal Haindl (Editor); Josef Kittler (Editor); Fabio Roli (Editor)
  • Binding Papeback
  • Edition U. S. EDITION
  • Condition New
  • Pages 524
  • Volumes 1
  • Language ENG
  • Publisher Springer , Berlin, Germany
  • Publication date pp. 540
  • Illustrated Yes
  • Features Bibliography, Illustrated, Index, Table of Contents
  • Bookseller's Inventory # 6306636
  • ISBN 9783540724810 / 3540724818
  • Weight 1.79 lbs (0.81 kg)
  • Dimensions 9.28 x 6.34 x 1.23 in (23.57 x 16.10 x 3.12 cm)
  • Category Computers - General Information
  • Dewey Decimal Code 004
  • Quantity available 4

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Reader reviews for Multiple Classifier Systems

From the publisher

These proceedings are a record of the Multiple Classi?er Systems Workshop, MCS 2007, held at the Institute of Information Theory and Automation, Czech Academy of Sciences, Prague in May 2007. Being the seventh in a well-established series of meetings providing an international forum for the discussion of issues in multiple classi?er system design, the workshop achieved its objective of bringing together researchers from diverse communities (neural networks, pattern rec- nition, machine learning and statistics) concerned with this research topic. From more than 80 submissions, the Programme Committee selected 49 - pers to create an interesting scienti?c programme. The special focus of MCS 2007 was on the application of multiple classi?er systems in biometrics. This part- ular application area exercises all aspects of multiple classi?er fusion, from - tramodal classi?er combination, through con?dence-based fusion, to multimodal biometric systems. The sponsorship of MCS 2007 by the European Union N- work of Excellence in Biometrics BioSecure and in Multimedia Understanding through Semantics, Computation and Learning MUSCLE and their assistance in selecting the contributions to the MCS 2007 programme consistent with this theme is gratefully acknowledged.
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