2023 - Research.com Computer Science in Germany Leader Award
2022 - Research.com Computer Science in Germany Leader Award
Internal medicine is often connected to Receiver operating characteristic in his work. His study on Receiver operating characteristic is mostly dedicated to connecting different topics, such as Internal medicine. His work in Cardiology is not limited to one particular discipline; it also encompasses Angina. In his works, Klaus-Robert Müller conducts interdisciplinary research on Angina and Ischemia. Klaus-Robert Müller performs integrative study on Ischemia and Unstable angina in his works. Klaus-Robert Müller conducted interdisciplinary study in his works that combined Unstable angina and Coronary artery disease. His research on Coronary artery disease frequently connects to adjacent areas such as Stable angina. His Stable angina study frequently draws connections to adjacent fields such as Cardiology. His work on Natriuretic peptide expands to the thematically related Heart failure.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Nonlinear component analysis as a kernel eigenvalue problem
Bernhard Schölkopf;Alexander Smola;Klaus-Robert Müller.
Neural Computation (1998)
An introduction to kernel-based learning algorithms
K.-R. Muller;S. Mika;G. Ratsch;K. Tsuda.
IEEE Transactions on Neural Networks (2001)
Efficient BackProp
Yann LeCun;Léon Bottou;Genevieve B. Orr;Klaus-Robert Müller.
neural information processing systems (1998)
Kernel Principal Component Analysis
Bernhard Schölkopf;Alex J. Smola;Klaus-Robert Müller.
international conference on artificial neural networks (1997)
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation.
Sebastian Bach;Alexander Binder;Grégoire Montavon;Frederick Klauschen.
PLOS ONE (2015)
Kernel Principal Component Analysis
Bernhard Schölkopf;Alexander J. Smola;Klaus-Robert Müller.
international conference on artificial neural networks (1997)
Optimizing Spatial filters for Robust EEG Single-Trial Analysis
B. Blankertz;R. Tomioka;S. Lemm;M. Kawanabe.
IEEE Signal Processing Magazine (2008)
Input space versus feature space in kernel-based methods
B. Scholkopf;S. Mika;C.J.C. Burges;P. Knirsch.
IEEE Transactions on Neural Networks (1999)
Soft Margins for AdaBoost
G. Rätsch;T. Onoda;K.-R. Müller.
Machine Learning (2001)
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.
Physical Review Letters (2012)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:
Technical University of Berlin
Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute
Technical University of Berlin
University of Luxembourg
Charité - University Medicine Berlin
ETH Zurich
The University of Texas at Austin
Max Planck Society
Wadsworth Center
Radboud University Nijmegen
University of Udine
ETH Zurich
Henri Poincaré Institute
Imperial College London
Technische Universität Braunschweig
University of Tsukuba
University of California, Berkeley
University of Otago
University of Illinois at Chicago
National Centre for Cell Science
Sinopec (China)
Heidelberg University
Northwestern University
George Washington University
Florida Maxima Corporation
Washington University in St. Louis