Martijn van Breukelen

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In classi"cation tasks it may be wise to combine observations from di!erent sources. Not only it decreases the training time but it can also increase the robustness and the performance of the classi"cation. Combining is often done by just (weighted) averaging of the outputs of the di!erent classi"ers. Using equal weights for all classi"ers then results in(More)
If a set of linear classifiers in the same feature spaces is combined by a linear output classifier and if each of these classifiers has a sigmoid output function then this set of classifiers has the same architecture as a feed-forward neural network. A combined set of classifiers, however, is trained in an entirely different way. In this paper it is shown(More)
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