Christian Moewes

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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)
Dynamic graphs are ubiquitous in real world applications. They can be found, e.g. in biology, neuroscience, computer science, medicine, social networks, the World Wide Web. There is a great necessity and interest in analyzing these dynamic graphs efficiently. Typically, analysis methods from classical data mining and network theory have been studied(More)
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)
This paper aims to motivate and demonstrate how widely available environmental data can be exploited to allow organization, structuring and exploration of music collections by personal listening contexts. We describe a logging plug-in for music players that automatically records data about the listening context and discuss possible extensions for more(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)
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)
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)
The idea of “smart sensing” includes a permanent monitoring and evaluation of sensor data related to possible measurement faults. This concept requires a fault detection chain covering all relevant fault types of a specific sensor. Additionally, the fault detection components have to provide a high precision in order to generate a reliable(More)
Designing and assembling automobiles is a complex task which has to be accomplished in ever shorter cycles. However, customers have increasing desires w. r. t. reliability, durability and comfort. In order to cope with these conflicting constraints it is indispensable to employ tools that greatly simplify the analysis of data that is collected during all(More)