Multiple Classifier Systems: 7th International Workshop, MCS 2007, Prague, Czech Republic, May 23-25, 2007. Proceedings

Vadim Mottl, Alexander Tatarchuk, Valentina Sulimova, Olga Krasotkina, Oleg Seredin (auth.), Michal Haindl, Josef Kittler, Fabio Roli (eds.)3540724818, 9783540724810, 9783540725237

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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Table of contents :
Front Matter….Pages –
Combining Pattern Recognition Modalities at the Sensor Level Via Kernel Fusion….Pages 1-12
The Neutral Point Method for Kernel-Based Combination of Disjoint Training Data in Multi-modal Pattern Recognition….Pages 13-21
Kernel Combination Versus Classifier Combination….Pages 22-31
Deriving the Kernel from Training Data….Pages 32-41
On the Application of SVM-Ensembles Based on Adapted Random Subspace Sampling for Automatic Classification of NMR Data….Pages 42-51
A New HMM-Based Ensemble Generation Method for Numeral Recognition….Pages 52-61
Classifiers Fusion in Recognition of Wheat Varieties….Pages 62-71
Multiple Classifier Methods for Offline Handwritten Text Line Recognition….Pages 72-81
Applying Data Fusion Methods to Passage Retrieval in QAS….Pages 82-92
A Co-training Approach for Time Series Prediction with Missing Data….Pages 93-102
An Improved Random Subspace Method and Its Application to EEG Signal Classification….Pages 103-112
Ensemble Learning Methods for Classifying EEG Signals….Pages 113-120
Confidence Based Gating of Colour Features for Face Authentication….Pages 121-130
View-Based Eigenspaces with Mixture of Experts for View-Independent Face Recognition….Pages 131-140
Fusion of Support Vector Classifiers for Parallel Gabor Methods Applied to Face Verification….Pages 141-150
Serial Fusion of Fingerprint and Face Matchers….Pages 151-160
Boosting Lite – Handling Larger Datasets and Slower Base Classifiers….Pages 161-170
Information Theoretic Combination of Classifiers with Application to AdaBoost….Pages 171-179
Interactive Boosting for Image Classification….Pages 180-189
Group-Induced Vector Spaces….Pages 190-199
Selecting Diversifying Heuristics for Cluster Ensembles….Pages 200-209
Unsupervised Texture Segmentation Using Multiple Segmenters Strategy….Pages 210-219
Classifier Ensembles for Vector Space Embedding of Graphs….Pages 220-230
Cascading for Nominal Data….Pages 231-240
A Combination of Sample Subsets and Feature Subsets in One-Against-Other Classifiers….Pages 241-250
Random Feature Subset Selection for Ensemble Based Classification of Data with Missing Features….Pages 251-260
Feature Subspace Ensembles: A Parallel Classifier Combination Scheme Using Feature Selection….Pages 261-270
Stopping Criteria for Ensemble-Based Feature Selection….Pages 271-281
On Rejecting Unreliably Classified Patterns….Pages 282-291
Bayesian Analysis of Linear Combiners….Pages 292-301
Applying Pairwise Fusion Matrix on Fusion Functions for Classifier Combination….Pages 302-311
Modelling Multiple-Classifier Relationships Using Bayesian Belief Networks….Pages 312-321
Classifier Combining Rules Under Independence Assumptions….Pages 322-332
Embedding Reject Option in ECOC Through LDPC Codes….Pages 333-343
On Combination of Face Authentication Experts by a Mixture of Quality Dependent Fusion Classifiers….Pages 344-356
Index Driven Combination of Multiple Biometric Experts for AUC Maximisation….Pages 357-366
Q  −  stack : Uni- and Multimodal Classifier Stacking with Quality Measures….Pages 367-376
Reliability-Based Voting Schemes Using Modality-Independent Features in Multi-classifier Biometric Authentication….Pages 377-386
Optimal Classifier Combination Rules for Verification and Identification Systems….Pages 387-396
Exploiting Diversity in Ensembles: Improving the Performance on Unbalanced Datasets….Pages 397-406
On the Diversity-Performance Relationship for Majority Voting in Classifier Ensembles….Pages 407-420
Hierarchical Behavior Knowledge Space….Pages 421-430
A New Dynamic Ensemble Selection Method for Numeral Recognition….Pages 431-439
Ensemble Learning in Linearly Combined Classifiers Via Negative Correlation….Pages 440-449
Naïve Bayes Ensembles with a Random Oracle….Pages 450-458
An Experimental Study on Rotation Forest Ensembles….Pages 459-468
Cooperative Coevolutionary Ensemble Learning….Pages 469-478
Robust Inference in Bayesian Networks with Application to Gene Expression Temporal Data….Pages 479-489
An Ensemble Approach for Incremental Learning in Nonstationary Environments….Pages 490-500
Multiple Classifier Systems in Remote Sensing: From Basics to Recent Developments….Pages 501-512
Biometric Person Authentication Is a Multiple Classifier Problem….Pages 513-522
Back Matter….Pages –