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TU Berlin

Inhalt des Dokuments

All Publications


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

Schöner, H., Stetter, M., Schießl, I., Mayhew, J., Lund, J., McLoughlin, N. and Obermayer, K. (2000). Application of Blind Separation of Sources to Optical Recording of Brain Activity. Advances in Neural Information Processing Systems 12. MIT Press, 949 – 955.,

Seo, S., Beck, A., Matthis, C., Genauck, A., Banaschewski, T., Bokde, A., Bromberg, U., Büchel, C., Quinlan, E., Flor, H., Frouin, V., Garavan, H., Gowland, P., Ittermann, B., Martinot, J., Martinot, M., Nees, F., Orfanos, D., Poustka, L., Hohmann, S., Froehner, J., Smolka, M., Walter, H., Whelan, R., Desrivieres, S., Heinz, A., Schumann, G. and Obermayer, K. (2019). Risk Profiles for Heavy Drinking in Adolescence: Differential Effects of Gender. Addiction Biology, 24, 787-801.

Seo, S., Mohr, J., Beck, A., Wüstenberg, T., Heinz, A. and Obermayer, K. (2015). Predicting the future relapse of alcohol-dependent patients from structural and functional brain images. Addiction Biology, 20, 1042-1055.

Seo, S., Bode, M. and Obermayer, K. (2003). Soft Nearest Prototype Classification. IEEE Transactions on Neural Networks, 14, 390 – 398.

Seo, S., Mohr, J., Ningfei, L., Horn, A. and Obermayer, K. (2015). Incremental pairwise clustering for large proximity matrices. 2015 International Joint Conference on Neural Networks (IJCNN), 1-8.,10.1109/IJCNN.2015.7280637

Seo, S., Mohr, J. and Obermayer, K. (2009). A New Incremental Pairwise Clustering Algorithm. Proceedings of the ICMLA -09: The Eighth International Conference on Machine Learning and Applications. IEEE, 223 – 228.,10.1109/ICMLA.2009.42

Seo, S. and Obermayer, K. (2006). Dynamic Hyperparameter Scaling Method for LVQ Algorithms. IJCNN 2006 Conference Proceedings. IEEE, 3196 – 3203.,10.1109/IJCNN.2006.247304

Seo, S. and Obermayer, K. (2004). Self-Organizing Maps and Clustering Methods for Matrix Data. Neural Networks Special Issue, 17, 1211 – 1229.

Seo, S. and Obermayer, K. (2003). Soft Learning Vector Quantization. Neural Computation, 15, 1589 – 1604.

Seo, S., Wallat, M., Graepel, T. and Obermayer, K. (2000). Gaussian Process Regression: Active Data Selection and Test Point Rejection. Neural Networks - IJCNN 2000. IEEE, 241 – 246.,10.1109/IJCNN.2000.861310

Seo, S., Mohr, J., Heekeren, H., Heinz, A., Eppinger, B., Li, S. and Obermayer, K. (2012). A voxel selection method for the multivariate analysis of imaging genetics data.. IJCNN. IEEE, 2884-2890.,10.1109/IJCNN.2012.6252766

Sheikh, A.-S., Shelton, J. A. and Lücke, J. (2014). A Truncated EM Approach for Spike-and-Slab Sparse Coding. Journal of Machine Learning Research, 15, 2653–2687.

Shelton, J. A., Sheikh, A.-S., Bornschein, J., Sterne, P. and Lücke, J. (2015). Nonlinear Spike-And-Slab Sparse Coding for Interpretable Image Encoding. PLoS ONE, 10, e0124088.

Shen, Y., Huang, R., Yan, C. and Obermayer, K. (2014). Risk-Averse Reinforcement Learning for Algorithmic Trading. 2014 IEEE Computational Intelligence for Financial Engineering and Economics, 391-398.,10.1109/CIFEr.2014.6924100

Shen, Y., Stannat, W. and Obermayer, K. (2014). A Unified Framework for Risk-sensitive Markov Control Processes. 53rd IEEE Conference on Decision and Control, 1073-1078.,10.1109/CDC.2014.7039524

Shen, Y., Stannat, W. and Obermayer, K. (2013). Risk-sensitive Markov Control Processes. SIAM Journal on Control and Optimization, 51, 3652–3672.

Shen, Y., Tobia, M. J., Sommer, T. and Obermayer, K. (2014). Risk-sensitive Reinforcement Learning. Neural Computation, 26, 1298-1328.

Spanagel, R., Durstewitz, D., Hansson, A., Heinz, A., Kiefer, F., Köhr, G., Matthäus, F., Nöthen, M. M., Noori, H. R., Obermayer, K., Rietschel, M., Schloss, P., Scholz, H., Schumann, G., Smolka, M., Sommer, W., Vengeliene, V., Walter, H., Wurst, W., Zimmermann, U. S., Group, A. G. R., Stringer, S., Smits, Y. and Derks, E. M. (2013). A systems medicine research approach for studying alcohol addiction. Addiction Biology, 18, 883–896.

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