Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal combinations of pattern classifiers
Pattern Recognition Letters
Machine Learning
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IEEE Transactions on Pattern Analysis and Machine Intelligence
A new hybrid approach in combining multiple experts to recognise handwritten numerals
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Journal of Artificial Intelligence Research
Morphologically Unbiased Classifier Combination through Graphical PDF Correlation
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Balancing the Role of Priors in Multi-Observer Segmentation Evaluation
Journal of Signal Processing Systems
The practical performance characteristics of tomographically filtered multiple classifier fusion
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Tomographic considerations in ensemble bias/variance decomposition
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
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We specify an analogy in which the various classifier combination methodologies are interpreted as the implicit reconstruction, by tomographic means, of the composite probability density function spanning the entirety of the pattern space, the process of feature selection in this scenario amounting to an extremely bandwidth-limited Radon transformation of the training data. This metaphor, once elaborated, immediately suggests techniques for improving the process, ultimately defining, in reconstructive terms, an optimal performance criterion for such combinatorial approaches.