Decision Combination in Multiple Classifier Systems
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
Lipreading: a classifier combination approach
Pattern Recognition Letters - special issue on pattern recognition in practice V
Experimental evaluation of expert fusion strategies
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
IEEE Transactions on Pattern Analysis and Machine Intelligence
Experiments with Classifier Combining Rules
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Application of majority voting to pattern recognition: an analysis of its behavior and performance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Improving model accuracy using optimal linear combinations of trained neural networks
IEEE Transactions on Neural Networks
On the Integration of Neural Classifiers through Similarity Analysis of Higher Order Features
ICDM '09 Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects
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Amidst the conflicting evidence of superiority of one over the other, we investigate the Sum and majority Vote combining rules for the two class case at a single point. We show analytically that, for Gaussian estimation error distributions, Sum always outperforms Vote, whereas for heavy tail distributions Vote may outperform Sum.