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Machine Learning

Conference Publications

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2015

Seo, S., Mohr, J., Ningfei, L., Horn, A. and Obermayer, K. (2015). Incremental pairwise clustering for large proximity matrices [6]. 2015 International Joint Conference on Neural Networks (IJCNN), 1-8.

Link to original publication [7]

2014

Mohr, J., Seo, S. and Obermayer, K. (2014). A classifier-based association test for imbalanced data derived from prediction theory [8]. 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 [9]. 2014 IEEE Computational Intelligence for Financial Engineering and Economics, 391-398.

Link to publication [10]

Shen, Y., Stannat, W. and Obermayer, K. (2014). A Unified Framework for Risk-sensitive Markov Control Processes [11]. 53rd IEEE Conference on Decision and Control, 1073-1078.

Link to publication [12]

2013

Böhmer, W. and Obermayer, K. (2013). Towards Structural Generalization: Factored Approximate Planning [13]. ICRA Workshop on Autonomous Learning

Link to publication [14]

2010

Jain, B. and Obermayer, K. (2010). Consistent Estimator of Median and Mean Graph [15]. Proceedings of the 2010 20th International Conference on Pattern Recognition. IEEE, 1032–1035.

Link to publication [16]

Jain, B. and Obermayer, K. (2010). Large Sample Statistics in the Domain of Graphs [17]. Structural, Syntactic, and Statistical Pattern Recognition. Springer Berlin Heidelberg, 690 – 697.

Link to original publication [18]

Jain, B., Srinivasan, S. D., Tissen, A. and Obermayer, K. (2010). Learning Graph Quantization [19]. Structural, Syntactic, and Statistical Pattern Recognition. Springer Berlin Heidelberg, 109 – 118.

Link to publication [20]

2009

Seo, S., Mohr, J. and Obermayer, K. (2009). A New Incremental Pairwise Clustering Algorithm [21]. Proceedings of the ICMLA -09: The Eighth International Conference on Machine Learning and Applications. IEEE, 223 – 228.

Link to original publication [22]

2008

Jain, B. and Obermayer, K. (2008). On the Sample Mean of Graphs [23]. 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence). IEEE, 993 – 1000.

Link to original publication [24]

2007

Jain, B. and Obermayer, K. (2007). Theory of the Sample Mean of Structures [25]. 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 [26]. SIGIR Workshop on Learning to Rank for Information Retrieval 2007, (1 – 8).

Link to publication [27]

Grünewälder, S. and Obermayer, K. (2007). Optimality of LSTD and its Relation to MC [28]. Neural Networks, IJCNN 2007, 338 – 343.

Link to original publication [29]

2006

Seo, S. and Obermayer, K. (2006). Dynamic Hyperparameter Scaling Method for LVQ Algorithms [30]. IJCNN 2006 Conference Proceedings. IEEE, 3196 – 3203.

Link to publication [31] Link to original publication [32]

Vollgraf, R. and Obermayer, K. (2006). Sparse Optimization for Second Order Kernel Methods [33]. IJCNN 2006 Conference Proceedings. IEEE, 145 – 152.

Link to original publication [34]

2005

Hochreiter, S. and Obermayer, K. (2005). Optimal Gradient-Based Learning Using Importance Weights [35]. Proceedings of the International Joint Conference on Neural Networks. IEEE, 114 – 119.

Link to original publication [36]

Hochreiter, S. and Obermayer, K. (2005). Optimal Kernels for Unsupervised Learning [37]. Proceedings of the International Joint Conference on Neural Networks, 1895 – 1899.

Link to original publication [38]

Mohr, J. and Obermayer, K. (2005). A Topographic Support Vector Machine: Classification Using Local Label Configurations [39]. Advances in Neural Information Processing Systems 17. MIT Press, 929 – 936.

Link to original publication [40]

2004

Vollgraf, R., Scholz, M., Meinertzhagen, I. and Obermayer, K. (2004). Nonlinear Filtering of Electron Micrographs by Means of Support Vector Regression [41]. Advances in Neural Information Processing Systems 16. MIT Press, 717 – 724.

Link to publication [42]

2003

Hochreiter, S., Mozer, M. and Obermayer, K. (2003). Coulomb Classifiers: Generalizing Support Vector Machines via an Analogy to Electrostatic Systems [43]. Advances in Neural Information Processing Systems 15. MIT Press, 561 – 568.

Link to publication [44]

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