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TU Berlin

Inhalt des Dokuments

Maschnielles Lernen

Konferenzpublikationen

2015

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.

Link zur Originalpublikation

2014

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.

Link zur Publikation

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.

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2013

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

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2010

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.

Link zur Publikation

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.

Link zur Originalpublikation

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.

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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.

Link zur Originalpublikation

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.

Link zur Originalpublikation

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).

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

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2006

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

Link zur Publikation Link zur Originalpublikation

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

Link zur Originalpublikation

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.

Link zur Originalpublikation

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

Link zur Originalpublikation

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.

Link zur Originalpublikation

2004

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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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.

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