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Machine Learning

All Publications

2003

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. (2003). Feature Selection and Classification on Matrix Data: From Large Margins To Small Covering Numbers. Advances in Neural Information Processing Systems 15. MIT Press, 913 – 920.,


Seo, S., Bode, M. and Obermayer, K. (2003). Soft Nearest Prototype Classification. IEEE Transactions on Neural Networks, 14, 390 – 398.


Seo, S. and Obermayer, K. (2003). Soft Learning Vector Quantization. Neural Computation, 15, 1589 – 1604.


Cuadros-Vargas, E., Romero, R. and Obermayer, K. (2003). Speeding up Algorithms of the SOM Family for Large and High Dimensional Databases. Proceedings WSOM, 167 – 172.,


2000

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


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


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.,


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


1999

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).,


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. (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. (1999). A Self-Organizing Map for Proximity Data. Neural Computation, 11, 139 – 155.


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. (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. (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.,


1998

Burger, M. a. G. T. and Obermayer, K. (1998). An Annealed Self-Organizing Map for Source Channel Coding. Advances in Neural Information Processing Systems 10. MIT Press, 430 – 436.,10.1.1.26.9359


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