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TU Berlin

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Machine Learning

All Publications

2009


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


2008

Jain, B. and Obermayer, K. (2008). On the Sample Mean of Graphs. 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence). IEEE, 993 – 1000.,10.1109/IJCNN.2008.4633920


Knebel, T., Hochreiter, S. and Obermayer, K. (2008). An SMO algorithm for the Potential Support Vector Machine. Neural Comput., 20, 271 – 287.


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.


Adiloglu, K., Annies, R., Henrich, F., Paus, A. and Obermayer, K. (2008). Geometrical Approaches to Active Learning. Autonomous Systems – Self-Organization, Management, and Control. Springer Netherlands, 11-19.,10.1007/978-1-4020-8889-6_2


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


2007

Jain, B. and Obermayer, K. (2007). Theory of the Sample Mean of Structures. LNVD 2007, Learning from Non-vectorial Data, 9-16.


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).,


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


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.


Vollgraf, R. and Obermayer, K. (2006). Sparse Optimization for Second Order Kernel Methods. IJCNN 2006 Conference Proceedings. IEEE, 145 – 152.,10.1109/IJCNN.2006.246672


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.,


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.,


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