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All Publications

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2000

Herbrich, R., Graepel, T. and Obermayer, K. (2000). Large Margin Rank Boundaries for Ordinal Regression [23]. Advances in Large Margin Classifiers. MIT Press, 115 – 132.,


1999

Krepki, R., Pu, Y., Meng, H. and Obermayer, K. (1999). A New Algorithm for the Interrogation of 3D Holographic Particle Tracking Velocimetry Data Based on Deterministic Annealing and EM-Optimizazion [24]. 3rd International Workshop on Particle Image Velocimetry. PIV '99, Santa Barbara, CA, USA, (1 – 14).,


Pielot, R., Scholz, M., Obermayer, K., Gundelfinger, E. and Hess, A. (1999). Optimiertes Warping durch gewichtete Summen von Verschiebungsvektoren - eine neue Methode zur Reduktion von interindividuellen Variabilitäten von Hirndaten [25]. Bildverarbeitung für die Medizin. Springer-Verlag Heidelberg, 417 – 421.,


Piepenbrock, C. and Obermayer, K. (1999). Effects of Lateral Competition in the Primary Visual Cortex on the Development of Topographic Projections and Ocular Dominance Maps [26]. Neurocomputing, 26-27, 477 – 482.


Piepenbrock, C. and Obermayer, K. (1999). The Role of Lateral Cortical Competition in Ocular Dominance Development [27]. Advances in Neural Information Processing Systems 11. MIT Press, 139 – 145.,


Schöner, H., Stetter, M., Schiessl, I., Mayhew, J., Lund, J., McLoughlin, N. and Obermayer, K. (1999). Blind Separation of Noisy Mixtures by Iterative Decorrelation. [28]. Proceedings. The Learning Workshop, Snowbird, USA, (1 – 2).,


Schiessl, I., Stetter, M., Mayhew, J., Askew, S., McLoughlin, N., Levitt, J., Lund, J. and Obermayer, K. (1999). Blind Separation of Spatial Signal Patterns from Optical Imaging Records [29]. ICA99 - International workshop on Idependent Component Analysis and Blind Source Separation, 179 – 184.,


Adorjan, P., Barna, G., Erdi, P. and Obermayer, K. (1999). A Statistical Neural Field Approach To Orientation Selectivity [30]. Neurocomputing, 26-27, 313 – 318.


Adorjan, P., Levitt, J., Lund, J. and Obermayer, K. (1999). A Model for the Intracortical Origin of Orientation Preference and Tuning in Macaque Striate Cortex [31]. Visual Neuroscience, 16, 303 – 318.


Adorjan, P. and Obermayer, K. (1999). Contrast Adaptation in Simple Cells by Changing the Transmitter Release Probability [32]. Advances in Neural Information Processing Systems 11. MIT Press.,


Adorjan, P., Piepenbrock, C. and Obermayer, K. (1999). Contrast Adaptation and Infomax in Visual Cortical Neurons [33]. Review Neuroscience, 10, 181 – 200.


Stetter, M. and Obermayer, K. (1999). Simulation of Scanning Laser Techniques for Optical Imaging of Blood-Related Intrinsic Signals [34]. Journal of the Optical Society of America, 16, 58 – 70.


Bartsch, H., Stetter, M. and Obermayer, K. (1999). On the Influence of Threshold Variability in a Model of the Visual Cortex [35]. Artificial Neural Networks - ICANN 99. IEEE, 73 – 78.,10.1049/cp:19991087


Bauer, U., Scholz, M., Levitt, J., Obermayer, K. and Lund, J. (1999). A Model for the Depth-Dependence of Receptive Field Size and Contrast Sensitivity of Cells in Layer 4C of Macaque Striate Cortex [36]. Vision Research, 39, 613 – 629.


Blankertz, B., Purwins, H. and Obermayer, K. (1999). Constant Q Profiles and Toroidal Models of Inter-Key Relations [37]. Proceedings of CMI-99, Conference on Musical Imagery, VI. International Conference on Systematic and Comparative Musicology, Oslo,


Weber, C. and Obermayer, K. (1999). Orientation Selective Cells Emerge in a Sparsely Coding Boltzmann Machine [38]. Artificial Neural Networks - ICANN 99. IEEE, 286 – 291.,10.1049/cp:19991123


Dima, A., Scholz, M. and Obermayer, K. (1999). Semi-Automatic Quality Determination of 3D Confocal Microscope Scans of Neuronal Cells Denoised by 3D-Wavelet Shrinkage [39]. Wavelet Applications VI - Proceedings of the SPIE, 446 – 457.,10.1117/12.342957


Graepel, T., Herbrich, R., Bollmann-Sdorra, P. and Obermayer, K. (1999). Classification on Pairwise Proximity Data [40]. Advances in Neural Information Processing Systems 11. MIT Press, 438 – 444.,


Graepel, T., Herbrich, R. and Obermayer, K. (1999). Bayesian transductive classification by maximizing volume in version space [41]. Proceedings of Learning 1999 Conference,


Graepel, T., Herbrich, R., Schoelkopf, B., Smola, A., Bartlett, P., Mueller, K., Obermayer, K. and Williamson, R. (1999). Classification on Proximity Data with LP-Machines [42]. 9th International Conference on Artificial Neural Networks - ICANN99. IEEE, 304 – 309.,10.1049/cp:19991126


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