Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Original Contribution: Stacked generalization
Neural Networks
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Optimal linear combinations of neural networks
Neural Networks
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical and neural classifiers: an integrated approach to design
Statistical and neural classifiers: an integrated approach to design
An Experimental Comparison of Fixed and Trained Fusion Rules for Crisp Classifier Outputs
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Multiple Classification Systems in the Context of Feature Extraction and Selection
MCS '02 Proceedings of the Third 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
Analysis of Linear and Order Statistics Combiners for Fusion of Imbalanced Classifiers
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Multiclassifier Systems: Back to the Future
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
k-nearest neighbors directed noise injection in multilayer perceptron training
IEEE Transactions on Neural Networks
An Experimental Comparison of Fixed and Trained Fusion Rules for Crisp Classifier Outputs
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Multiple Classification Systems in the Context of Feature Extraction and Selection
MCS '02 Proceedings of the Third 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
Real-Time Gesture Recognition by Learning and Selective Control of Visual Interest Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolving ensemble of classifiers in random subspace
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Trainable fusion rules. I. Large sample size case
Neural Networks
Trainable fusion rules. II. Small sample-size effects
Neural Networks
Pairwise fusion matrix for combining classifiers
Pattern Recognition
A genetic encoding approach for learning methods for combining classifiers
Expert Systems with Applications: An International Journal
Applying pairwise fusion matrix on fusion functions for classifier combination
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Reducing the overconfidence of base classifiers when combining their decisions
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
An experimental study of one- and two-level classifier fusion for different sample sizes
Pattern Recognition Letters
Generalization error of multinomial classifier
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Semi-supervised multiple classifier systems: background and research directions
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
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We consider the trainable fusion rule design problem when the expert classifiers provide crisp outputs and the behavior space knowledge method is used to fuse local experts' decisions. If the training set is utilized to design both the experts and the fusion rule, the experts' outputs become too self-assured. In small sample situations, "optimistically biased" experts' outputs bluffs the fusion rule designer. If the experts differ in complexity and in classification performance, then the experts' boasting effect and can severely degrade the performance of a multiple classification system. Theoretically-based and experimental procedures are suggested to reduce the experts' boasting effect.