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Machine Learning

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Hochreiter, S., Mozer, M. and Obermayer, K. (2003). Coulomb Classifiers: Generalizing Support Vector Machines via an Analogy to Electrostatic Systems [9]. Advances in Neural Information Processing Systems 15. MIT Press, 561 – 568.,


Hochreiter, S. and Obermayer, K. (2006). Nonlinear Feature Selection with the Potential Support Vector Machine [10]. Feature Extraction: Foundations and Applications. Springer Berlin Heidelberg, 419 – 438.,10.1007/978-3-540-35488-8_20


Hochreiter, S. and Obermayer, K. (2005). Optimal Gradient-Based Learning Using Importance Weights [11]. 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 [12]. Proceedings of the International Joint Conference on Neural Networks, 1895 – 1899.,10.1109/IJCNN.2005.1556169


Hochreiter, S. and Obermayer, K. (2003). Feature Selection and Classification on Matrix Data: From Large Margins To Small Covering Numbers [13]. Advances in Neural Information Processing Systems 15. MIT Press, 913 – 920.,


Jain, B. and Obermayer, K. (2011). Maximum Likelihood for Gaussians on Graphs [14]. Graph-Based Representations in Pattern Recognition. Springer Berlin Heidelberg, 62-71.,10.1007/978-3-642-20844-7_7


Jain, B. and Obermayer, K. (2011). Generalized Learning Graph Quantization [15]. Graph-Based Representations in Pattern Recognition. Springer Berlin Heidelberg, 122-131.,10.1007/978-3-642-20844-7_13


Jain, B. and Obermayer, K. (2010). Elkan’s k-Means Algorithm for Graphs [16]. Advances in Soft Computing. Springer Berlin Heidelberg, 22-32.,10.1007/978-3-642-16773-7_2


Jain, B. and Obermayer, K. (2010). Consistent Estimator of Median and Mean Graph [17]. Proceedings of the 2010 20th International Conference on Pattern Recognition. IEEE, 1032–1035.,10.1109/ICPR.2010.258


Jain, B. and Obermayer, K. (2010). Large Sample Statistics in the Domain of Graphs [18]. Structural, Syntactic, and Statistical Pattern Recognition. Springer Berlin Heidelberg, 690 – 697.,10.1007/978-3-642-14980-1_10


Jain, B. and Obermayer, K. (2009). Algorithms for the Sample Mean of Graphs [19]. Lecture Notes in Computer Science, 351 – 359.,


Jain, B., Srinivasan, S. D., Tissen, A. and Obermayer, K. (2010). Learning Graph Quantization [20]. Structural, Syntactic, and Statistical Pattern Recognition. Springer Berlin Heidelberg, 109 – 118.,10.1007/978-3-642-14980-1_10


Jain, B. and Obermayer, K. (2008). On the Sample Mean of Graphs [21]. 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence). IEEE, 993 – 1000.,10.1109/IJCNN.2008.4633920


Mohr, J. and Obermayer, K. (2005). A Topographic Support Vector Machine: Classification Using Local Label Configurations [22]. Advances in Neural Information Processing Systems 17. MIT Press, 929 – 936.,


Mohr, J., Seo, S. and Obermayer, K. (2014). A classifier-based association test for imbalanced data derived from prediction theory [23]. Neural Networks (IJCNN), 2014 International Joint Conference on, 487-493.,10.1109/IJCNN.2014.6889547


Obermayer, K. (1992). Neural Pattern Formation and Self-Organizing Maps [24]. Annales de Groupe CARNAC 5, 91 – 104.,


Srinivasan, D. and Obermayer, K. (2011). Probabilistic prototype models for attributed graphs [25]. ,


Shen, Y., Grünewälder, S. and Obermayer, K. (2011). A Unified Framework for Risk-sensitive Markov Decision Processes with Finite State and Action Spaces [26]. ,


Jain, B. and Obermayer, K. (2011). Extending Bron Kerbosch for Solving the Maximum Weight Clique Problem [27]. ,


Jain, B. and Obermayer, K. (2010). Accelerating Competetive Learning Graph Quantization [28]. ,


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