Herward Prehn

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In this paper we present an adaptive classification method that features a robust, efficient and simple to use incremental clustering algorithm. A new assignment strategy for incorporating new data patterns allows clusters to align more exhaustively with the data structure. This almost eliminates the sensitivity to the order of input data, many incremental(More)
In this paper we propose the Local Credibility Concept (LCC), a novel technique for incremental classifiers. It measures the classification rate of the classifier's local models and ensures that the models do not cross the borders between classes, but allows them to develop freely within the domain of their own class. Thus, we reduce the dependency on the(More)
We demonstrate an optical Fourier filtering method which can be used to characterize subcellular morphology during dynamic cellular function. In this paper, our Fourier filters were based on two-dimensional Gabor elementary functions, which can be tuned to sense directly object size and orientation. We utilize this method to quantify changes in(More)
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