K
Klaus-Robert Müller
Researcher at Technical University of Berlin
Publications - 799
Citations - 98394
Klaus-Robert Müller is an academic researcher from Technical University of Berlin. The author has contributed to research in topics: Artificial neural network & Computer science. The author has an hindex of 129, co-authored 764 publications receiving 79391 citations. Previous affiliations of Klaus-Robert Müller include Korea University & University of Tokyo.
Papers
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Journal ArticleDOI
Open Access Dataset for EEG+NIRS Single-Trial Classification
Jaeyoung Shin,Alexander von Lühmann,Benjamin Blankertz,Do-Won Kim,Jichai Jeong,Han-Jeong Hwang,Klaus-Robert Müller +6 more
TL;DR: An open access dataset for hybrid brain–computer interfaces (BCIs) using electroencephalography (EEG) and near-infrared spectroscopy (NIRS) is provided.
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Understanding machine‐learned density functionals
Li Li,John C. Snyder,John C. Snyder,Isabelle M. Pelaschier,Isabelle M. Pelaschier,Jessica Huang,Uma Naresh Niranjan,Paul Duncan,Matthias Rupp,Klaus-Robert Müller,Klaus-Robert Müller,Kieron Burke +11 more
TL;DR: In this paper, Kernel ridge regression is used to approximate the kinetic energy of non-interacting fermions in a one dimensional box as a functional of their density, and a projected gradient descent algorithm is derived using local principal component analysis.
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Combining sparsity and rotational invariance in EEG/MEG source reconstruction.
Stefan Haufe,Vadim V. Nikulin,Andreas Ziehe,Andreas Ziehe,Klaus-Robert Müller,Klaus-Robert Müller,Guido Nolte +6 more
TL;DR: Compared to its peers FVR was the only method that delivered correct location of the source in the somatosensory area of each hemisphere in accordance with neurophysiological prior knowledge.
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Improving the Robustness of Myoelectric Pattern Recognition for Upper Limb Prostheses by Covariate Shift Adaptation
Marina M.-C. Vidovic,Han-Jeong Hwang,Sebastian Amsüss,Janne M. Hahne,Dario Farina,Klaus-Robert Müller +5 more
TL;DR: The proposed supervised adaptation methods can contribute to improve robustness of myoelectric pattern recognition methods in daily life applications through the use of adapted classifier using a small calibration set only.
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Input-Dependent Estimation of Generalization Error under Covariate Shift
TL;DR: This paper proposes an alternative estimator of the generalization error for the squared loss function when training and test distributions are different and is shown to be exactly unbiased for finite samples if the learning target function is realizable and asymptotically unbiased in general.