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Machine Learning

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Lohoff, F., Lautenschlager, M., Mohr, J., Ferraro, T., Sander, T. and Gallinat, J. (2008). Association Between Variation in the Vesicular Monoamine Transporter 1 Gene on Chromosome 8p and Anxiety-Related Personality Traits. Neuroscience Letters, 434, 41 – 45.


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Mohr, J. and Obermayer, K. (2005). A Topographic Support Vector Machine: Classification Using Local Label Configurations. Advances in Neural Information Processing Systems 17. MIT Press, 929 – 936.,


Mohr, J., Seo, S. and Obermayer, K. (2014). A classifier-based association test for imbalanced data derived from prediction theory. Neural Networks (IJCNN), 2014 International Joint Conference on, 487-493.,10.1109/IJCNN.2014.6889547


Müller, L., Ploner, M., Goerttler, T. and Obermayer, K. (2021). An Interactive Introduction to Model-Agnostic Meta-Learning. Workshop on Visualization for AI Explainability at IEEE VIS


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Obermayer, K. (1992). Neural Pattern Formation and Self-Organizing Maps. Annales de Groupe CARNAC 5, 91 – 104.,


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Ritter, H., Obermayer, K. and Rubner, J. (1991). Self-Organizing Maps and Adaptive Filters. Physics of Neural Networks. Springer, 281 – 306.,


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Scheel, C., Neubauer, N., Lommatzsch, A., Obermayer, K. and Albayrak, S. (2007). Efficient Query Delegation by Detecting Redundant Retrieval Strategies. SIGIR Workshop on Learning to Rank for Information Retrieval 2007, (1 – 8).,


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).,


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


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


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