Gert. R. G. Lanckriet

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This is the supplemental material for the CVPR 2010 paper, “Clustering Dynamic Textures with the Hierarchical EM Algorithm” [1]. It contains the derivation of the HEM-DTM algorithm and the associated E-step computations, including sensitivity analysis for the Kalman smoothing filter. 1 Derivation of HEM for dynamic textures In this section we derive the HEM(More)
The objective of this study was to optimally predict the spontaneous passage of ureteral stones in patients with renal colic by applying for the first time support vector machines (SVM), an instance of kernel methods, for classification. After reviewing the results found in the literature, we compared the performances obtained with logistic regression (LR)(More)
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