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  • Conference proceedings
  • © 2005

Multiple Classifier Systems

6th International Workshop, MCS 2005, Seaside, CA, USA, June 13-15, 2005, Proceedings

Editors:

  1. Nikunj C. Oza

    1. NASA Ames Research Center, Moffett Field, USA

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  2. Robi Polikar

    1. Signal Processing and Pattern Recognition Laboratory, Electrical and Computer Engineering, Rowan University, Glassboro, USA

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  3. Josef Kittler

    1. Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK

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  4. Fabio Roli

    1. University of Cagliari, Department of Electrical and Electronic Engineering, Cagliari, Italy

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 3541)

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

Conference series link(s): MCS: International Workshop on Multiple Classifier Systems

Conference proceedings info: MCS 2005.

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Table of contents (42 papers)

  1. Front Matter

    PDF

  3. Boosting

    1. Boosting GMM and Its Two Applications

      • Fei Wang, Changshui Zhang, Naijiang Lu
      Pages 12-21

    3. Observations on Boosting Feature Selection

      • D. B. Redpath, K. Lebart
      Pages 32-41

  4. Combination Methods

    1. Decoding Rules for Error Correcting Output Code Ensembles

      • R. S. Smith, T. Windeatt
      Pages 53-63

    2. A Probability Model for Combining Ranks

      • Ofer Melnik, Yehuda Vardi, Cun-Hui Zhang
      Pages 64-73

    4. Mixture of Gaussian Processes for Combining Multiple Modalities

      • Ashish Kapoor, Hyungil Ahn, Rosalind W. Picard
      Pages 86-96

    5. Dynamic Classifier Integration Method

      • E. Kim, J. Ko
      Pages 97-107

    6. Recursive ECOC for Microarray Data Classification

      • Elizabeth Tapia, Esteban Serra, José Carlos González
      Pages 108-117

    7. Using Dempster-Shafer Theory in MCF Systems to Reject Samples

      • Christian Thiel, Friedhelm Schwenker, Günther Palm
      Pages 118-127

    8. Multiple Classifier Fusion Performance in Networked Stochastic Vector Quantisers

      • R. Patenall, D. Windridge, J. Kittler
      Pages 128-135

    9. On Deriving the Second-Stage Training Set for Trainable Combiners

      • Pavel Paclík, Thomas C. W. Landgrebe, David M. J. Tax, Robert P. W. Duin
      Pages 136-146

    10. Using Independence Assumption to Improve Multimodal Biometric Fusion

      • Sergey Tulyakov, Venu Govindaraju
      Pages 147-155

  5. Design Methods

    1. Half-Against-Half Multi-class Support Vector Machines

      • Hansheng Lei, Venu Govindaraju
      Pages 156-164

    2. Combining Feature Subsets in Feature Selection

      • Marina Skurichina, Robert P. W. Duin
      Pages 165-175

    3. ACE: Adaptive Classifiers-Ensemble System for Concept-Drifting Environments

      • Kyosuke Nishida, Koichiro Yamauchi, Takashi Omori
      Pages 176-185

    4. Using Decision Tree Models and Diversity Measures in the Selection of Ensemble Classification Models

      • Mordechai Gal-Or, Jerrold H. May, William E. Spangler
      Pages 186-195
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Keywords

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Editors and Affiliations

  • NASA Ames Research Center, Moffett Field, USA

    Nikunj C. Oza

  • Signal Processing and Pattern Recognition Laboratory, Electrical and Computer Engineering, Rowan University, Glassboro, USA

    Robi Polikar

  • Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK

    Josef Kittler

  • University of Cagliari, Department of Electrical and Electronic Engineering, Cagliari, Italy

    Fabio Roli

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Bibliographic Information

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Access via your institution

Buy it now

eBook USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

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