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https://doi.org/10.1007/978-3-030-88892-3nt quality. We use GIS data to extract the structure of each river and link this structure to 81 river water stations (that measure both water temperature and discharge). Since the water temperature of a river is strongly dependent on the air temperature, we also include 44 weather stations (which m

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Quadratic Kernel Learning for Interpolation Kernel Machine Based Graph Classificationhigh-performance ensemble techniques. Interpolation kernel machines belong to the class of interpolating classifiers and do generalize well. They have been demonstrated to be a good alternative to support vector machine for graph classification. In this work we further improve their performance by c

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Graph-Based vs. Vector-Based Classification: A Fair Comparison and applications is crucial. In this paper, we conduct a comprehensive assessment of three commonly used graph-based classifiers across 24 graph datasets (we employ classifiers based on graph matchings, graph kernels, and graph neural networks). Our goal is to find out what primarily affects the pe

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A Practical Algorithm for Max-Norm Optimal Binary Labeling of Graphslski (2020). This method finds, in quadratic time with respect to graph size, a labeling that globally minimizes an objective function based on the .-norm. The method enables global optimization for a novel class of optimization problems, with high relevance in application areas such as image proces

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