Christian Moewes

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In this paper we reason about the usefulness of two recent trends in fuzzy methods in machine learning. That is, we discuss both fuzzy support vector machines (FSVMs) and the extraction of fuzzy rules from SVMs. First, we show that an FSVM is identical to a special type of SVM. Second, we categorize and analyze existing approaches to obtain fuzzy rules from(More)
OBJECTIVE To characterize brain functional connectivity in subjects with prechiasmatic visual system damage and relate functional connectivity features to extent of vision loss. METHODS In this case-control study, resting-state, eyes-closed EEG activity was recorded in patients with partial optic nerve damage (n = 15) and uninjured controls (n = 13). We(More)
In this paper we introduce a preprocessing method for safety-related applications. Since we concentrate on scenarios with highly unbalanced misclas-sification costs, we briefly discuss a variation of multiple-instance learning (MIL) and recall soft margin hyperplane classifiers; in particular the principle of a support vector machine (SVM). According to(More)
Fuzzy control accounts for the biggest industrial success of fuzzy logic. We review an interpretation of Mamdani's heuristic control approach. It can be seen as knowledge-based interpolation based on input-output points of a vaguely known function. We reexamine two real-world control problems that have been fortunately solved based on this interpretation.
We present an approach to learn fuzzy binary decision rules from ordinal temporal data where the task is to classify every instance at each point in time. We assume that one class is preferred to the other, e.g. the undesirable class must not be misclassified. Hence it is appealing to use the Variable Consistency Dominance-based Rough Set Approach (VC-DRSA)(More)
In neuroscience it became popular to represent neuroimaging data from the human brain as networks. The edges of these (weighted) graphs represent a spatio-temporal similarity between paired data channels. The temporal series of graphs is commonly averaged to a weighted graph of which edge weights are eventually thresholded. Graph measures are then applied(More)