Skip to main content
SpringerLink
Log in

Multiple Classifier Systems

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

  • Conference proceedings
  • © 2005

Overview

Editors:
  1. Nikunj C. Oza

    1. NASA Ames Research Center, Moffett Field, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Robi Polikar

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

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  3. Josef Kittler

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

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  4. Fabio Roli

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

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

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)

Included in the following conference series:

Conference proceedings info: MCS 2005.

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
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

Licence this eBook for your library

Institutional subscriptions

Table of contents (42 papers)

  1. Front Matter

    Download chapter PDF

  3. Boosting

    1. Boosting GMM and Its Two Applications

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

    2. Boosting Soft-Margin SVM with Feature Selection for Pedestrian Detection

      • Kenji Nishida, Takio Kurita
      Pages 22-31

    3. Observations on Boosting Feature Selection

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

    4. Boosting Multiple Classifiers Constructed by Hybrid Discriminant Analysis

      • Qi Tian, Jie Yu, Thomas S. Huang
      Pages 42-52

  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

    3. EER of Fixed and Trainable Fusion Classifiers: A Theoretical Study with Application to Biometric Authentication Tasks

      • Norman Poh, Samy Bengio
      Pages 74-85

    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
Back to top

Other volumes

  1. Multiple Classifier Systems

Keywords

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

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation