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Machine Learning

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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., 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


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


Graepel, T. and Obermayer, K. (1999). A Self-Organizing Map for Proximity Data. Neural Computation, 11, 139 – 155.


Grünwälder, S. and Obermayer, K. (2011). The Optimal Unbiased Extimator and its Relation to LSTD, TD and MC. Machine Learning, 83, 289 – 330.


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


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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.,


Henniges, M., Turner, R. E., Sahani, M., Eggert, J. and Lücke, J. (2014). Efficient Occlusive Components Analysis. Journal of Machine Learning Research, 15, 2689–2722.


Henrich, F. and Obermayer, K. (2008). Active Learning by Spherical Subdivision. Journal of Machine Learning Research, 9, 105 – 130.


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.,


Hochreiter, J. and Obermayer, K. (2006). Support Vector Machines for Dyadic Data. Neural Comput., 18, 1472 – 1510.


Hochreiter, S., Mozer, M. and Obermayer, K. (2003). Coulomb Classifiers: Generalizing Support Vector Machines via an Analogy to Electrostatic Systems. Advances in Neural Information Processing Systems 15. MIT Press, 561 – 568.,


Hochreiter, S. and Obermayer, K. (2006). Nonlinear Feature Selection with the Potential Support Vector Machine. Feature Extraction: Foundations and Applications. Springer Berlin Heidelberg, 419 – 438.,10.1007/978-3-540-35488-8_20


Hochreiter, S. and Obermayer, K. (2005). Optimal Gradient-Based Learning Using Importance Weights. Proceedings of the International Joint Conference on Neural Networks. IEEE, 114 – 119.,10.1109/IJCNN.2005.1555815


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