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Machine Learning

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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.


Srinivasan, D. and Obermayer, K. (2011). Probabilistic prototype models for attributed graphs. ,


Svensson, C.-M., Krusekopf, S., Lücke, J. and Figge, M. T. (2014). Automated Detection of Circulating Tumour Cells With Naive Bayesian Classifiers. Cytometry Part A, 85, 501–511.


Südholt, M., Piepenbrock, C., Obermayer, K. and Pepper, P. (1997). Solving Large Systems of Differential Equations using Convolutions by Transformation. IFIP Working Conference on Algorithmic Languages and Calculi, Strasbourg. Chapman \\& Hall, (1 – 27).,


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Tobia, M. J., Guo, R., Schwarze, U., Böhmer, W., Gläscher, J., Finckh, B., Marschner, A., Büchel, C., Obermayer, K. and Sommer, T. (2014). Neural Systems for Choice and Valuation with Counterfactual Learning Signals. NeuroImage, 89, 57-69.


Trowitzsch, I., Mohr, J., Kashef, Y. and Obermayer, K. (2017). Robust Detection of Environmental Sounds in Binaural Auditory Scenes. IEEE Transactions on Audio Speech and Language Processing, 25, 1344-1356.


Trowitzsch I., Schymura C., Kolossa D. and K., O. (2019). Joining Sound Event Detection and Localization Through Spatial Segregation. IEEE Trans. Audio Speech Language Proc.


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Vollgraf, R. and Obermayer, K. (2006). Quadratic Optimization for Simultaneous Matrix Diagonalization. IEEE Trans. Signal Processing Applications, 54, 3270 – 3278.


Vollgraf, R. and Obermayer, K. (2006). Sparse Optimization for Second Order Kernel Methods. IJCNN 2006 Conference Proceedings. IEEE, 145 – 152.,10.1109/IJCNN.2006.246672


Vollgraf, R., Scholz, M., Meinertzhagen, I. and Obermayer, K. (2004). Nonlinear Filtering of Electron Micrographs by Means of Support Vector Regression. Advances in Neural Information Processing Systems 16. MIT Press, 717 – 724.,


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