Blar i forfatter "Müller, Klaus R."

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    • A Scatter-Based Prototype Framework and Multi-Class Extension of Support Vector Machines 

      Jenssen, Robert; Kloft, Marius; Zien, Alexander; Sonnenburg, Soeren; Müller, Klaus R. (Journal article; Tidsskriftartikkel; Peer reviewed, 2012)
      We provide a novel interpretation of the dual of support vector machines (SVMs) in terms of scatter with respect to class prototypes and their mean. As a key contribution, we extend this framework to multiple classes, providing a new joint Scatter SVM algorithm, at the level of its binary counterpart in the number of optimization variables. This enables us to implement computationally efficient ...

    • Unification of sparse Bayesian learning algorithms for electromagnetic brain imaging with the majorization minimization framework 

      Hashemi, Ali; Cai, Chang; Kutyniok, Gitta Astrid Hildegard; Müller, Klaus R.; Nagarajan, Srikantan S.; Haufe, Stefan (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-10-01)
      Methods for electro- or magnetoencephalography (EEG/MEG) based brain source imaging (BSI) using sparse Bayesian learning (SBL) have been demonstrated to achieve excellent performance in situations with low numbers of distinct active sources, such as event-related designs. This paper extends the theory and practice of SBL in three important ways. First, we reformulate three existing SBL algorithms ...