Algorithms for clustering data
Algorithms for clustering data
Conceptual Modeling of Coincident Failures in Multiversion Software
IEEE Transactions on Software Engineering
A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Network generalization differences quantified
Neural Networks
Pattern Recognition Letters - Special issue on non-conventional pattern analysis in remote sensing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Knowledge reuse in multiple classifier systems
Pattern Recognition Letters - special issue on pattern recognition in practice V
IEEE Transactions on Pattern Analysis and Machine Intelligence
Engineering multiversion neural-net systems
Neural Computation
Combinations of weak classifiers
IEEE Transactions on Neural Networks
Cascade Classifier: Design and Application to Digit Recognition
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
A modular reduction method for k-NN algorithm with self-recombination learning
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
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In the field of pattern recognition, multiple classifier systems based on the combination of the outputs of a set of different classifiers have been proposed as a method for the development of high performance classification systems. Previous work clearly showed that multiple classifier systems are effective only if the classifiers forming them make independent errors. This achievement pointed out the fundamental need for methods aimed to design ensembles of "independent" classifiers. However, the most of the recent work focused on the development of combination methods. In this paper, an approach to the automatic design of multiple classifier systems based on unsupervised learning is proposed. Given an initial set of classifiers, such approach is aimed to identify the largest subset of "independent" classifiers. A proof of the optimality of the proposed approach is given. Reported results on the classification of remote sensing images show that this approach allows one to design effective multiple classifier systems.