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TU Berlin

Inhalt des Dokuments

All Publications

H

Hochreiter, S. and Obermayer, K. (2006). Nonlinear Feature Selection with the Potential Support Vector Machine. 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. Kernel Methods in Computational Biology. MIT Press, 319 – 356.,


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.,10.1109/IJCNN.2005.1555815


Hochreiter, S. and Obermayer, K. (2005). Optimal Kernels for Unsupervised Learning. 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. 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. Proceedings of the International Statistical Institute, (1 – 4).,


Holca-Lamarre, R., Luecke, J. and Obermayer, K. (2017). Acetylcholine and Dopamine Signals Differentially Improve Sensory Representations in a Neural Network Model. Front. Comput. Neurosci., 11


Houillon, A., Lorenz, R. C., Boehmer, W., Rapp, M. A., Heinz, A., Gallinat, J. and Obermayer, K. (2013). The effect of novelty on reinforcement learning. Progress in brain research, 202, 415–439.


Hutter, F., Lücke, J. and Schmidt-Thieme, L. (2015). Beyond Manual Tuning of Hyperparameters. KI - Künstliche Intelligenz, 29, 329-337.


Huys, Q., Deserno, L., Obermayer, K., Schlagenhauf, F. and Heinz, A. (2016). Model-free temporal-difference learning and dopamine in alcohol dependence: examining concepts from theory and animals in human imaging. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 1, 401 - 410.


J

Jain, B. and Obermayer, K. (2007). Theory of the Sample Mean of Structures. LNVD 2007, Learning from Non-vectorial Data, 9-16.


Jain, B. and Obermayer, K. (2011). Maximum Likelihood for Gaussians on Graphs. 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. 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. 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. 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. 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. 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. Lecture Notes in Computer Science, 351 – 359.,


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.,10.1007/978-3-642-14980-1_10


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


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