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Machine Learning

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Erwin, E., Obermayer, K. and Schulten, K. (1991). Convergence Properties of Self-organizing Maps. Artificial Neural Networks I. North Holland, 409 – 414.,


Grünewälder, S. and Obermayer, K. (2007). Optimality of LSTD and its Relation to MC. Neural Networks, IJCNN 2007, 338 – 343.,


Graepel, T., Burger, M. and Obermayer, K. (1997). Deterministic Annealing for Topographic Vector Quantization and Self-Organizing Maps. Proceedings of the Workshop on Self-Organizing Maps - WSOM 97, 345 – 350.,


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


Graepel, T., Herbrich, R. and Obermayer, K. (2000). Bayesian Transduction. Advances in Neural Information Processing Systems 12. MIT Press, 456 – 462.,


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


Graepel, T. and Obermayer, K. (1998). Fuzzy Topographic Kernel Clustering. Proceedings of the 5th GI Workshop Fuzzy Neuro Systems, 90 – 97.,


Hasenjäger, M., Ritter, H. and Obermayer, K. (1999). Active Data Selection for Fuzzy Topographic Mapping of Proximities. Fuzzy-Neuro Systems 1999 - Computational Intelligence, 93–104.,


Hasenjäger, M., Ritter, H. and Obermayer, K. (2000). Active Data Selection for Topographic Pairwise Clustering. Classification, Automation, and New Media. Program of the 24th Annual Conference of the German Classification Society (GfKl), 80.,


Hasenjäger, M., Ritter, H. and Obermayer, K. (1999). Active Topographic Mapping of Proximities. 9th International Conference on Artificial Neural Networks - ICANN99. IEEE, 952 – 957.,10.1049/cp:19991235


Hasenjäger, M., Ritter, H. and Obermayer, K. (1999). Active Learning in Self-Organizing Maps. Kohonen Maps. Elsevier, 57–70.,


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


Herbrich, R., Graepel, T. and Obermayer, K. (1999). Support Vector Learning for Ordinal Regression. 9th International Conference on Artificial Neural Networks - ICANN99. IEEE, 97 – 102.,10.1049/cp:19991091


Herbrich, R., Keilbach, M., Graepel, T., Bollmann-Sdorra, P. and Obermayer, K. (1999). Neural Networks in Economics: Background, Applications and New Developments. Advances in Computational Economics: Computational Techniques for Modelling Learning in Economics. Kluwer Academics, 169 – 196.,


1

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. 9th International Conference on Artificial Neural Networks - ICANN99. IEEE, 304 – 309.,10.1049/cp:19991126


B

Erwin, E., Obermayer, K. and Schulten, K. (1992). Self-Organizing Maps: Ordering, Convergence Properties and Energy Functions. Biological Cybernetics, 67, 47 – 55.


Erwin, E., Obermayer, K. and Schulten, K. (1992). Self-Organizing Maps: Stationary States, Metastability and Convergence Rate. Biological Cybernetics, 67, 35 – 45.


C

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.


I

Böhmer, W. and Obermayer, K. (2013). Towards Structural Generalization: Factored Approximate Planning. ICRA Workshop on Autonomous Learning


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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