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Machine Learning

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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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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.


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.


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.


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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).,


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.


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



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.


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.


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.


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


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