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Prof. Dr. Klaus-Robert Müller

 

Bild von Müller, Klaus-Robert, Prof. Dr.

Klaus-Robert Müller

Position: Wissenschaftler  Abteilung: 

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Bücher (1):

Lee S-W, Bülthoff HH und Müller K-R: Recent Progress in Brain and Cognitive Engineering, 213, Springer, Dordrecht, The Netherlands, (2015). ISBN: 978-94-017-7238-9, Series: Trends in Augmentation of Human Performance ; 5

Artikel (20):

Kim J, Müller K-R, Chung YG, Chung S-C, Park J-Y, Bülthoff HH und Kim S-P (Januar-2015) Distributed functions of detection and discrimination of vibrotactile stimuli in the hierarchical human somatosensory system Frontiers in Human Neuroscience 8(1070) 1-10.
Biessmann F, Murayama Y, Logothetis NK, Müller KR und Meinecke FC (Juli-2012) Improved decoding of neural activity from fMRI signals using non-separable spatiotemporal deconvolutions NeuroImage 61(4) 1031–1042.
Murayama Y, Biessmann F, Meinecke FC, Müller K-R, Augath M, Oeltermann A und Logothetis NK (Oktober-2010) Relationship between neural and hemodynamic signals during spontaneous activity studied with temporal kernel CCA Magnetic Resonance Imaging 28(8) 1095-1103.
Baehrens D, Schroeter T, Harmeling S, Kawanabe M, Hansen K und Müller K-R (Juni-2010) How to Explain Individual Classification Decisions Journal of Machine Learning Research 11 1803-1831.
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Biessmann F, Meinecke FC, Gretton A, Rauch A, Rainer G, Logothetis NK und Müller K-R (Mai-2010) Temporal Kernel CCA and its Application in Multimodal Neuronal Data Analysis Machine Learning 79(1-2) 5-27.
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Sonnenburg S, Braun ML, Ong CS, Bengio S, Bottou L, Holmes G, LeCun Y, Müller K-R, Pereira F, Rasmussen CE, Rätsch G, Schölkopf B, Smola A, Vincent P, Weston J und Williamson RC (Oktober-2007) The Need for Open Source Software in Machine Learning Journal of Machine Learning Research 8 2443-2466.
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Harmeling S, Dornhege G, Tax D, Meinecke F und Müller K-R (August-2006) From outliers to prototypes: Ordering data Neurocomputing 69(13-15) 1608-1618.
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Meinecke F, Harmeling S und Müller K-R (Juli-2005) Inlier-based ICA with an application to superimposed images International Journal of Imaging Systems and Technology 15(1) 48-55.
Harmeling S, Meinecke F und Müller K-R (Februar-2004) Injecting noise for analysing the stability of ICA components Signal Processing 84(2) 255-266.
Tsuda K, Akaho S, Kawanabe M und Müller KR (Januar-2004) Asymptotic Properties of the Fisher Kernel Neural Computation 16(1) 115-137.
Ziehe A, Kawanabe M, Harmeling S und Müller K-R (November-2003) Blind separation of post-nonlinear mixtures using linearizing transformations and temporal decorrelation Journal of Machine Learning Research 4(7-8) 1319-1338.
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Mika S, Rätsch G, Weston J, Schölkopf B, Smola AJ und Müller K-R (Mai-2003) Constructing descriptive and discriminative nonlinear features: Rayleigh coefficients in kernel feature spaces IEEE Transactions on Pattern Analysis and Machine Intelligence 25(5) 623-628.
Harmeling S, Ziehe A, Kawanabe M und Müller K-R (Mai-2003) Kernel-based nonlinear blind source separation Neural Computation 15(5) 1089-1124.
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Rätsch G, Mika S, Schölkopf B und Müller K-R (September-2002) Constructing Boosting algorithms from SVMs: an application to one-class classification IEEE Transactions on Pattern Analysis and Machine Intelligence 24(9) 1184-1199.
Müller K-R, Mika S, Rätsch G, Tsuda K und Schölkopf B (März-2001) An Introduction to Kernel-Based Learning Algorithms IEEE Transactions on Neural Networks 12(2) 181-201.
Zien A, Rätsch G, Mika S, Schölkopf B, Lengauer T und Müller K-R (September-2000) Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites Bioinformatics 16(9) 799-807.
Schölkopf B, Mika S, Burges CJC, Knirsch P, Müller K-R, Rätsch G und Smola AJ (September-1999) Input space versus feature space in kernel-based methods IEEE Transactions On Neural Networks 10(5) 1000-1017.
Schölkopf B, Müller K-R und Smola AJ (September-1999) Lernen mit Kernen: Support-Vektor-Methoden zur Analyse hochdimensionaler Daten Informatik - Forschung und Entwicklung 14(3) 154-163.
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Schölkopf B, Smola AJ und Müller K-R (Juli-1998) Nonlinear Component Analysis as a Kernel Eigenvalue Problem Neural Computation 10(5) 1299-1319.
Smola AJ, Schölkopf B und Müller K-R (Juni-1998) The connection between regularization operators and support vector kernels Neural Networks 11(4) 637-649.

Beiträge zu Tagungsbänden (30):

Biessmann F, Murayama Y, Logothetis NK, Müller K-R und Meinecke FC (2012) Non-separable Spatiotemporal Brain Hemodynamics Contain Neural Information In: Machine Learning and Interpretation in Neuroimaging, , NIPS Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI 2011), Springer, Berlin, Germany, 140-147, Series: Lecture Notes in Computer Science ; 7263.
Laub J, Macke JH, Müller K-R und Wichmann FA (September-2007) Inducing Metric Violations in Human Similarity Judgements In: Advances in Neural Information Processing Systems 19, , Twentieth Annual Conference on Neural Information Processing Systems (NIPS 2006), MIT Press, Cambridge, MA, USA, 777-784.
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Meinecke F, Harmeling S und Müller K-R (Oktober-2004) Robust ICA for Super-Gaussian Sources In: Independent Component Analysis and Blind Signal Separation, , Fifth International Conference on Independent Component Analysis and Blind Signal Separation (ICA 2004), Springer, Berlin, Germany, 217-224, Series: Lecture Notes in Computer Science ; 3195.
Harmeling S, Meinecke F und Müller K-R (April-2003) Analysing ICA component by injection noise, 4th International Symposium on Independent Component Analysis and Blind Signal Separation (ICA 2003), Tokyo, Japan, 149-154.
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Ziehe A, Kawanabe M, Harmeling S und Müller K-R (April-2003) Blind separation of post-nonlinear mixtures using gaussianizing transformations and temporal decorrelation, 4th International Symposium on Independent Component Analysis and Blind Signal Separation (ICA 2003), Tokyo, Japan, 269-274.
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Tsuda K, Kawanabe M, Rätsch G, Sonnenburg S und Müller K-R (September-2002) A new discriminative kernel from probabilistic models In: Advances in Neural Information Processing Systems 14, , Fifteenth Annual Neural Information Processing Systems Conference (NIPS 2001), MIT Press, Cambridge, MA, USA, 977-984.
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Harmeling S, Ziehe A, Kawanabe M und Müller K-R (September-2002) Kernel feature spaces and nonlinear blind source separation In: Advances in Neural Information Processing Systems 14, , Fifteenth Annual Neural Information Processing Systems Conference (NIPS 2001), MIT Press, Cambridge, MA, USA, 761-768.
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Sonnenburg S, Rätsch G, Jagota A und Müller K-R (August-2002) New methods for Splice Site recognition In: Artificial Neural Networks - ICANN 2002, , International Conference on Artificial Neural Networks, Springer, Berlin, Germany, 329-336, Series: Lecture Notes in Computer Science ; 2415.
Harmeling S, Ziehe A, Kawanabe M, Blankertz B und Müller K-R (Dezember-2001) Nonlinear blind source separation using kernel feature spaces, Third International Workshop on Independent Component Analysis and Blind Signal Separation (ICA 2001), 102-107.
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Ziehe A, Kawanabe M, Harmeling S und Müller K-R (Dezember-2001) Separation of post-nonlinear mixtures using ACE and temporal decorrelation, Third International Workshop on Independent Component Analysis and Blind Signal Separation (ICA 2001), 433-438.
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Tsuda K, Rätsch G, Mika S und Müller K-R (August-2001) Learning to predict the leave-one-out error of kernel based classifiers In: Artificial Neural Networks - ICANN 2001, , International Conference on Artificial Neural Networks, Springer, Berlin, Germany, 331-338, Series: Lecture Notes in Computer Science ; 2130.
Rätsch G, Warmuth MK, Mika S, Onoda T, Lemm S und Müller K-R (Juli-2000) Barrier Boosting, 13th Annual Conference on Computational Learning Theory (COLT 2000), Morgan Kaufmann, San Francisco, CA, USA, 170-179.
Mika S, Rätsch G, Weston J, Schölkopf B, Smola AJ und Müller K-R (Juni-2000) Invariant feature extraction and classification in kernel spaces In: Advances in neural information processing systems 12, , Thirteenth Annual Neural Information Processing Systems Conference (NIPS 1999), MIT Press, Cambridge, MA, USA, 526-532.
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Rätsch G, Schölkopf B, Smola AJ, Müller K-R, Onoda T und Mika S (Juni-2000) v-Arc: Ensemble Learning in the Presence of Outliers In: Advances in Neural Information Processing Systems 12, , Thirteenth Annual Neural Information Processing Systems Conference (NIPS 1999), MIT Press, Cambridge, MA, USA, 561-567.
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Rätsch G, Schölkopf B, Smola AJ, Mika S, Onoda T und Müller K-R (April-2000) Robust Ensemble Learning for Data Mining In: Knowledge Discovery and Data Mining: Current Issues and New Applications, , Fourth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2000), Springer, Berlin, Germany, 341-344, Series: Lecture Notes in Artificial Intelligence ; 1805.
Rätsch G, Schölkopf B, Smola AJ, Mika S, Onoda T und Müller K-R (2000) Robust ensemble learning In: Advances in Large Margin Classifiers, , NIPS 1998 Workshop “Advances in Large Margin Classifiers”, MIT Press, Cambridge, MA, USA, 207-220, Series: Neural Information Processing Series.
Graepel T, Herbrich R, Schölkopf B, Smola AJ, Bartlett P, Müller K, Obermayer K und Williamson RC (September-1999) Classification on proximity data with LP-machines, Ninth International Conference on Artificial Neural Networks (ICANN 99), Institute of Electrical Engineers, London, UK, 304-309, Series: Conference Publication of the Institution of Electrical Engineers ; 470.
Mika S, Rätsch G, Weston J, Schölkopf B und Müller K-R (August-1999) Fisher discriminant analysis with kernels In: Neural networks for signal processing IX, , 1999 IEEE Signal Processing Society Workshop, IEEE, Piscataway, NJ, USA, 41-48.
Mika S, Schölkopf B, Smola AJ, Müller K-R, Scholz M und Rätsch G (Juni-1999) Kernel PCA and De-noising in feature spaces In: Advances in Neural Information Processing Systems 11, , Twelfth Annual Conference on Neural Information Processing Systems (NIPS 1998), MIT Press, Cambridge, MA, USA, 536-542.
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Rätsch G, Onoda T und Müller KR (Juni-1999) Regularizing AdaBoost In: Advances in Neural Information Processing Systems 11, , Twelfth Annual Conference on Neural Information Processing Systems (NIPS 1998), MIT Press, Cambridge, MA, USA, 564-570.
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Vannerem P, Müller K-R, Smola AJ, Schölkopf B und Söldner-Rembold S (April-1999) Classifying LEP data with support vector algorithms, Conference on Artificial Intelligence in High Energy Nuclear Physics (AIHENP '99), 1-7.
Schölkopf B, Smola AJ und Müller K-R (1999) Kernel principal component analysis In: Advances in kernel methods: support vector learning, , Eleventh Annual Conference on Neural Information Processing (NIPS 1997), MIT Press, Cambridge, MA, USA, 327-352.
Müller K-R, Smola AJ, Rätsch G, Schölkopf B, Kohlmorgen J und Vapnik V (1999) Using support vector machines for time series prediction In: Advances in kernel methods: support vector learning, , Eleventh Annual Conference on Neural Information Processing (NIPS 1997), MIT Press, Cambridge, MA, USA, 243-253.
Smola AJ, Murata N, Schölkopf B und Müller K-R (September-1998) Asymptotically Optimal Choice of ε-Loss for Support Vector Machines In: ICANN 98, , 8th International Conference on Artificial Neural Networks, Springer, Berlin, Germany, 105-110, Series: Perspectives in Neural Computing.
Smola AJ, Schölkopf B und Müller K-R (September-1998) Convex Cost Functions for Support Vector Regression In: ICANN 98, , 8th International Conference on Artificial Neural Networks, Springer, Berlin, Germany, 8th International Conference on Artificial Neural Networks, 99-104, Series: Perspectives in Neural Computing.
Schölkopf B, Mika S, Smola AJ, Rätsch G und Müller K-R (September-1998) Kernel PCA pattern reconstruction via approximate pre-images In: ICANN 98, , 8th International Conference on Artificial Neural Networks, Springer, Berlin, Germany, 8th International Conference on Artificial Neural Networks, 147-152, Series: Perspectives in Neural Computing.
Smola AJ, Schölkopf B und Müller K-R (Februar-1998) General cost functions for Support Vector Regression, Ninth Australian Conference on Neural Networks (ACNN'98), University of Queensland, Brisbane, Australia, Ninth Australian Conference on Neural Networks, 79-83.
Schölkopf B, Smola AJ, Müller K-R, Burges C und Vapnik V (Februar-1998) Support Vector methods in learning and feature extraction, Ninth Australian Conference on Neural Networks (ACNN'98), University of Queensland, Brisbane, Australia, 72-78.
Schölkopf B, Smola AJ und Müller K-R (Oktober-1997) Kernel principal component analysis In: Artificial Neural Networks - ICANN '97, , 7th International Conference on Artificial Neural Networks, Springer, Berlin, Germany, 583-588, Series: Lecture Notes in Computer Science ; 1327.
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Last updated: Montag, 22.05.2017