An interactive two-level architecture for a memory network pattern classifier
Pattern Recognition Letters
Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Floating search methods in feature selection
Pattern Recognition Letters
A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Divergence Based Feature Selection for Multimodal Class Densities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Strategies for combining classifiers employing shared and distinct pattern representations
Pattern Recognition Letters - special issue on pattern recognition in practice V
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive floating search methods in feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Structural Matching in Computer Vision Using Probabilistic Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Novel Methods for Subset Selection with Respect to Problem Knowledge
IEEE Intelligent Systems
Improving Statistical Measures of Feature Subsets by Conventional and Evolutionary Approaches
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Combination of Multiple Classifiers Using Local Accuracy Estimates
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Learning Support Vectors for Face Verification and Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Model Complexity Validation for PDF Estimation Using Gaussian Mixtures
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Minimum Complexity PDF Estimation for Correlated Data
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Automatic Classification of Clustered Microcalcifications by a Multiple Classifier System
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Methods for Designing Multiple Classifier Systems
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Reduction of the Boasting Bias of Linear Experts
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Confidence Evaluation for Combining Diverse Classifiers
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Classifier Fusion Using Shared Sampling Distribution for Boosting
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Benefit of multiclassifier systems for Arabic handwritten words recognition
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Handwritten digits recognition improved by multiresolution classifier fusion
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
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We consider the problem and issues of classifier fusion and discuss how they should be reflected in the fusion system architecture. We adopt the Bayesian viewpoint and show how this leads to classifier output moderation to compensate for sampling problems. We then discuss how the moderated outputs should be combined to reflect the prior distribution of models underlying the classifier designs.We then elaborate how the final stage of fusion should combine the complementary measurement information that might be available to different experts. This process is embodied in an overall architecture which shows why the fusion of raw expert outputs is a nonlinear function of the expert outputs and how this function can be realised as a sequence of relatively simple processes.