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Ladenbauer, J., Augustin, M. and Obermayer, K. (2016). Intrinsic Control Mechanisms of Neuronal Network Dynamics [22]. Control of Self-Organizing Nonlinear Systems. Springer International Publishing, 441-460.,10.1007/978-3-319-28028-8_23


Hochreiter, S., Mozer, M. and Obermayer, K. (2003). Coulomb Classifiers: Generalizing Support Vector Machines via an Analogy to Electrostatic Systems [23]. 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 [24]. Feature Extraction: Foundations and Applications. Springer Berlin Heidelberg, 419 – 438.,10.1007/978-3-540-35488-8_20


Hochreiter, S. and Obermayer, K. (2004). Gene Selection for Microarray Data [25]. Kernel Methods in Computational Biology. MIT Press, 319 – 356.,


Hochreiter, S. and Obermayer, K. (2005). Optimal Gradient-Based Learning Using Importance Weights [26]. 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 [27]. 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 [28]. Advances in Neural Information Processing Systems 15. MIT Press, 913 – 920.,


Hochreiter, S. and Obermayer, K. (2003). Classification and Feature Selection on Matrix Data with Application to Gene-Expression Analysis [29]. Proceedings of the International Statistical Institute, (1 – 4).,


Jain, B. and Obermayer, K. (2011). Maximum Likelihood for Gaussians on Graphs [30]. 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 [31]. 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 [32]. 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 [33]. 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 [34]. 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). Bimal: Bipartite Matching Alignment for the Contact Map Overlap Problem [35]. 2009 International Joint Conference on Neural Networks. IEEE, 1394 – 1400.,10.1109/IJCNN.2009.5178901


Jain, B. and Obermayer, K. (2009). Algorithms for the Sample Mean of Graphs [36]. Lecture Notes in Computer Science, 351 – 359.,


Jain, B., Srinivasan, S. D., Tissen, A. and Obermayer, K. (2010). Learning Graph Quantization [37]. Structural, Syntactic, and Statistical Pattern Recognition. Springer Berlin Heidelberg, 109 – 118.,10.1007/978-3-642-14980-1_10


Jain, B., Stehr, H., Lappe, M. and Obermayer, K. (2009). Multiple Alignment of Contact Maps [38]. 2009 International Joint Conference on Neural Networks. IEEE, 1401 – 1406.,10.1109/IJCNN.2009.5178907


Jain, B. and Obermayer, K. (2008). On the Sample Mean of Graphs [39]. 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence). IEEE, 993 – 1000.,10.1109/IJCNN.2008.4633920


Kim, H., Obermayer, K., Bode, M. and Ruswisch, D. (2001). A 1.2KBPS Speech Codec Using Spectral Vector Quantization of Differential Feature Vectors [40]. Proceedings ICSP, 304.,


Kim, H., Obermayer, K., Bode, M. and Ruwisch, D. (2001). Efficient Speech Enhancement by Diffusive Gain Factors (DGF) [41]. Proc. Eurospeech 2001, 1867 – 1870.,


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