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
Optimizing Spatial filters for Robust EEG Single-Trial Analysis
Benjamin Blankertz,Ryota Tomioka,S. Lemm,Motoaki Kawanabe,Klaus-Robert Müller,Klaus-Robert Müller +5 more
TL;DR: The theoretical background of the common spatial pattern (CSP) algorithm, a popular method in brain-computer interface (BCD research), is elucidated and tricks of the trade for achieving a powerful CSP performance are revealed.
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Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Matthias Rupp,Matthias Rupp,Alexandre Tkatchenko,Alexandre Tkatchenko,Klaus-Robert Müller,Klaus-Robert Müller,O. Anatole von Lilienfeld,O. Anatole von Lilienfeld +7 more
TL;DR: A machine learning model is introduced to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only, and applicability is demonstrated for the prediction of molecular atomization potential energy curves.
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Soft Margins for AdaBoost
TL;DR: It is found that ADABOOST asymptotically achieves a hard margin distribution, i.e. the algorithm concentrates its resources on a few hard-to-learn patterns that are interestingly very similar to Support Vectors.
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Input space versus feature space in kernel-based methods
Bernhard Schölkopf,Sebastian Mika,C.J.C. Burges,P. Knirsch,Klaus-Robert Müller,Gunnar Rätsch,Alexander J. Smola +6 more
TL;DR: The geometry of feature space is reviewed, and the connection between feature space and input space is discussed by dealing with the question of how one can, given some vector in feature space, find a preimage in input space.