Publications
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Non-negative sparse coding for general blind source separation. Workshop NIPS 2003 (2003).
Neural representation of multi-dimensional stimuli. NIPS \'2000: Advances in Neural Information Processing Systems 12, 115–121 (2000).
Prediction of financial data with Hidden Markov mixtures of experts. International Journal of Theoretical and Applied Finance 3 (3), 593 (2000).
Fast change point detection in switching dynamics using a Hidden Markov model of prediction experts. ICANN\'99 Proceedings 204–209 (IEE, London, 1999).
Hidden Markov gating for prediction of change points in switching dynamical systems. ESANN \'99: Proceedings 405–410 (D-Facto, 1999).
Hidden Markov mixtures of experts for prediction of non–stationary dynamics. NNSP \'99: Workshop on Neural Networks for Signal Processing 195–204 (IEEE, NY, 1999).
Hidden Markov mixtures of experts with an application to EEG recordings from sleep. Theory in Biosciences 118 (3–4), 246–260 (1999).
Identification of non-stationary dynamics in physiological recordings. Biological Cybernetics 83, 73–84 (1999).
Analysis of drifting dynamics with neural network hidden Markov models. NIPS \'97: Advances in Neural Information Processing Systems 10 735–741 (1998).
Analysis of wake/sleep EEG with competing experts. ICANN \'97 Proceedings of the Int. Conf. on Artificial Neural Networks ( ) 1077–1082 (Springer, Berlin, 1997).
Divisive strategies for predicting non-autonomous and mixed systems. Technical Report No. 1069, GMD (1997).
Segmentation and identification of drifting dynamical systems. NNSP \'97: IEEE Workshp on Neural Networks for Signal Processing ( ) 326–335 (IEEE, 1997).
Verfahren zur Erfassung zeitabhängiger Moden dynamischer Systeme. Patent, Gemeinschaftserfindung, angemeldet am 15.09.1997, Az 197 40 565.7 (1997).
Analysis of drifting dynamics with competing predictors. ICANN \'96: Artificial Neural Networks ( ) 785–790 (Springer, 1996).
Prediction of mixtures. ICANN \'96: Artificial Neural Networks ( ) 127–132 (Springer, 1996).
Analysing physiological data from the wake-sleep state transition with competing predictors. Proceedings of the NOLTA, Las Vegas 223–226 (1995).
Analysis of switching dynamics with competing neural networks. IEICE Transaction on Fundamentals of Electronics, Communications and Computer Sciences E 78-A (10), 1306–1315 (1995).
Improving short-term prediction with competing experts. ICANN \'95 Proceedings ( ) 2, 215–220 (EC2 & Cie, Paris, 1995).
Competing predictors segment and identify switching dynamics. ICANN \'94 Proceedings ( ) 1045–1048 (Springer, 1994).
Segmentation and identification of switching dynamics with competing neural networks. ICONIP \'94: Proceedings of the Int. Conf. on Neural Information Processing, Seoul 213–218 (1994).
Verhandlungen der Deutschen Physikalischen Gesellschaft 5 (29), 927 (1994).
The use of competing neural networks for segmentation and identification of switching dynamics. NOLTA \'94: Kagoshima Symposion on Nonlinear Theory and its Applications 283–287 (1994).