TY - JOUR A1 - Laskov, Pavel A1 - Gehl, Christian A1 - Krüger, Stefan A1 - Müller, Klaus-Robert T1 - Incremental support vector learning: analysis, implementation and applications JF - Journal of machine learning research N2 - Incremental Support Vector Machines (SVM) are instrumental in practical applications of online learning. This work focuses on the design and analysis of efficient incremental SVM learning, with the aim of providing a fast, numerically stable and robust implementation. A detailed analysis of convergence and of algorithmic complexity of incremental SVM learning is carried out. Based on this analysis, a new design of storage and numerical operations is proposed, which speeds up the training of an incremental SVM by a factor of 5 to 20. The performance of the new algorithm is demonstrated in two scenarios: learning with limited resources and active learning. Various applications of the algorithm, such as in drug discovery, online monitoring of industrial devices and and surveillance of network traffic, can be foreseen. KW - incremental SVM KW - online learning KW - drug discovery KW - intrusion detection Y1 - 2006 SN - 1532-4435 VL - 7 SP - 1909 EP - 1936 PB - MIT Press CY - Cambridge, Mass. ER - TY - JOUR A1 - Dornhege, Guido A1 - Blankertz, Benjamin A1 - Krauledat, Matthias A1 - Losch, Florian A1 - Curio, Gabriel A1 - Müller, Klaus-Robert T1 - Combined optimization of spatial and temporal filters for improving brain-computer interfacing JF - IEEE transactions on bio-medical electronics N2 - Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output de ice by bypassing conventional motor output pathways of nerves and muscles. Therefore they could provide a new communication and control option for paralyzed patients. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. Here we present a novel technique that allows the simultaneous optimization of a spatial and a spectral filter enhancing discriminability rates of multichannel EEG single-trials. The evaluation of 60 experiments involving 22 different subjects demonstrates the significant superiority of the proposed algorithm over to its classical counterpart: the median classification error rate was decreased by 11%. Apart from the enhanced classification, the spatial and/or the spectral filter that are determined by the algorithm can also be used for further analysis of the data, e.g., for source localization of the respective brain rhythms. KW - brain-computer interface KW - common spatial patterns KW - EEG KW - event-related desynchronization KW - single-trial-analysis Y1 - 2006 U6 - https://doi.org/10.1109/TBME.2006.883649 SN - 0018-9294 VL - 53 IS - 11 SP - 2274 EP - 2281 PB - IEEE CY - New York ER - TY - JOUR A1 - Mieth, Bettina A1 - Kloft, Marius A1 - Rodriguez, Juan Antonio A1 - Sonnenburg, Soren A1 - Vobruba, Robin A1 - Morcillo-Suarez, Carlos A1 - Farre, Xavier A1 - Marigorta, Urko M. A1 - Fehr, Ernst A1 - Dickhaus, Thorsten A1 - Blanchard, Gilles A1 - Schunk, Daniel A1 - Navarro, Arcadi A1 - Müller, Klaus-Robert T1 - Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies JF - Scientific reports N2 - The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008-2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0. Y1 - 2016 U6 - https://doi.org/10.1038/srep36671 SN - 2045-2322 VL - 6 PB - Nature Publ. Group CY - London ER - TY - JOUR A1 - Montavon, Gregoire A1 - Braun, Mikio L. A1 - Krüger, Tammo A1 - Müller, Klaus-Robert T1 - Analyzing local structure in Kernel-Based learning JF - IEEE signal processing magazine Y1 - 2013 U6 - https://doi.org/10.1109/MSP.2013.2249294 SN - 1053-5888 VL - 30 IS - 4 SP - 62 EP - 74 PB - Inst. of Electr. and Electronics Engineers CY - Piscataway ER - TY - JOUR A1 - Blankertz, Benjamin A1 - Müller, Klaus-Robert A1 - Krusienski, Dean A1 - Schalk, Gerwin A1 - Wolpaw, Jonathan R. A1 - Schlögl, Alois A1 - Pfurtscheller, Gert A1 - Millan, José del R. A1 - Schröder, Michael A1 - Birbaumer, Niels T1 - The BCI competition III : validating alternative approaches to actual BCI problems N2 - A brain-computer interface (BCI) is a system that allows its users to control external devices with brain activity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands is still a major challenge. Success requires the effective interaction of two adaptive controllers: the user's brain, which produces brain activity that encodes intent, and the BCI system, which translates that activity into device control commands. In order to facilitate this interaction, many laboratories are exploring a variety of signal analysis techniques to improve the adaptation of the BCI system to the user. In the literature, many machine learning and pattern classification algorithms have been reported to give impressive results when applied to BCI data in offline analyses. However, it is more difficult to evaluate their relative value for actual online use. BCI data competitions have been organized to provide objective formal evaluations of alternative methods. Prompted by the great interest in the first two BCI Competitions, we organized the third BCI Competition to address several of the most difficult and important analysis problems in BCI research. The paper describes the data sets that were provided to the competitors and gives an overview of the results. Y1 - 2006 UR - http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7333 U6 - https://doi.org/10.1109/Tnsre.2006.875642 SN - 1534-4320 ER - TY - JOUR A1 - Harmeling, Stefan A1 - Dornhege, Guido A1 - Tax, David A1 - Meinecke, Frank C. A1 - Müller, Klaus-Robert T1 - From outliers to prototypes : Ordering data N2 - We propose simple and fast methods based on nearest neighbors that order objects from high-dimensional data sets from typical points to untypical points. On the one hand, we show that these easy-to-compute orderings allow us to detect outliers (i.e. very untypical points) with a performance comparable to or better than other often much more sophisticated methods. On the other hand, we show how to use these orderings to detect prototypes (very typical points) which facilitate exploratory data analysis algorithms such as noisy nonlinear dimensionality reduction and clustering. Comprehensive experiments demonstrate the validity of our approach. Y1 - 2006 UR - http://www.sciencedirect.com/science/journal/09252312 U6 - https://doi.org/10.1016/j.neucom.2005.05.015 SN - 0925-2312 ER - TY - JOUR A1 - Onoda, T. A1 - Rätsch, Gunnar A1 - Müller, Klaus-Robert T1 - An asymptotic analysis and improvement of AdaBoost in the binary classification case (in Japanese) Y1 - 2000 ER - TY - JOUR A1 - Rätsch, Gunnar A1 - Schölkopf, B. A1 - Smola, Alexander J. A1 - Mika, Sebastian A1 - Onoda, T. A1 - Müller, Klaus-Robert T1 - Robust ensemble learning for data analysis Y1 - 2000 ER - TY - JOUR A1 - Kohlmorgen, J. A1 - Müller, Klaus-Robert A1 - Rittweger, J. A1 - Pawelzik, K. T1 - Identification of nonstationary dynamics in physiological recordings Y1 - 2000 ER - TY - JOUR A1 - Mika, Sebastian A1 - Rätsch, Gunnar A1 - Weston, J. A1 - Schölkopf, B. A1 - Smola, Alexander J. A1 - Müller, Klaus-Robert T1 - Invariant feature extraction and classification in kernel spaces Y1 - 2000 ER -