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Machine Learning

All Publications

1999

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.,


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.,


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.,


2004

Seo, S. and Obermayer, K. (2004). Self-Organizing Maps and Clustering Methods for Matrix Data. Neural Networks Special Issue, 17, 1211 – 1229.


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.,


2005

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


Hochreiter, S. and Obermayer, K. (2005). Optimal Kernels for Unsupervised Learning. Proceedings of the International Joint Conference on Neural Networks, 1895 – 1899.,10.1109/IJCNN.2005.1556169


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.,


2006

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


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


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


Vollgraf, R. and Obermayer, K. (2006). Quadratic Optimization for Simultaneous Matrix Diagonalization. IEEE Trans. Signal Processing Applications, 54, 3270 – 3278.


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