Maschnielles Lernen

Seo, S., Mohr, J., Ningfei, L., Horn, A. and Obermayer, K. (2015). Incremental pairwise clustering for large proximity matrices. 2015 International Joint Conference on Neural Networks (IJCNN), 1-8.

Mohr, J., Seo, S. and Obermayer, K. (2014). A classifier-based association test for imbalanced data derived from prediction theory. Neural Networks (IJCNN), 2014 International Joint Conference on, 487-493.

Shen, Y., Huang, R., Yan, C. and Obermayer, K. (2014). Risk-Averse Reinforcement Learning for Algorithmic Trading. 2014 IEEE Computational Intelligence for Financial Engineering and Economics, 391-398.

Shen, Y., Stannat, W. and Obermayer, K. (2014). A Unified Framework for Risk-sensitive Markov Control Processes. 53rd IEEE Conference on Decision and Control, 1073-1078.

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

Jain, B. and Obermayer, K. (2010). Consistent Estimator of Median and Mean Graph. Proceedings of the 2010 20th International Conference on Pattern Recognition. IEEE, 1032–1035.

Jain, B. and Obermayer, K. (2010). Large Sample Statistics in the Domain of Graphs. Structural, Syntactic, and Statistical Pattern Recognition. Springer Berlin Heidelberg, 690 – 697.

Jain, B., Srinivasan, S. D., Tissen, A. and Obermayer, K. (2010). Learning Graph Quantization. Structural, Syntactic, and Statistical Pattern Recognition. Springer Berlin Heidelberg, 109 – 118.

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.

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.

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.

Seo, S. and Obermayer, K. (2006). Dynamic Hyperparameter Scaling Method for LVQ Algorithms. IJCNN 2006 Conference Proceedings. IEEE, 3196 – 3203.

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

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.

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

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.

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.

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.