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Inhalt des Dokuments

Analyse neuronaler Daten



Mahler, G. and Obermayer, K. (1987). Towars the Quantum Computer: Information Processing with Single Electrons. Computational Systems - Natural and Artificial. Springer, 154 – 165.

Link zur Originalpublikation


Ritter, H., Obermayer, K. and Rubner, J. (1991). Self-Organizing Maps and Adaptive Filters. Physics of Neural Networks. Springer, 281 – 306.

Link zur Originalpublikation


Hasenjäger, M., Ritter, H. and Obermayer, K. (1999). Active Learning in Self-Organizing Maps. Kohonen Maps. Elsevier, 57–70.

Link zur Publikation

Herbrich, R., Keilbach, M., Graepel, T., Bollmann-Sdorra, P. and Obermayer, K. (1999). Neural Networks in Economics: Background, Applications and New Developments. Advances in Computational Economics: Computational Techniques for Modelling Learning in Economics. Kluwer Academics, 169 – 196.

Link zur Publikation


Obermayer, K. (2000). Modeling the Formation of Sensory Representations in the Brain. Prerational Intelligence: Adaptive Behavior and Intelligent Systems Without Symbols and Logic, Volume 1, Volume 2 Prerational Intelligence: Interdisciplinary Perspectives on the Behavior of Natural and Artificial Systems, Volume 3. Springer Netherlands, 215 – 232.

Link zur Originalpublikation

Stetter, M. and Obermayer, K. (2000). Biology and Theory of Early Vision in Mammals. Brains and Biological Neural Networks. INNS Press, (1 – 50).

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Herbrich, R., Graepel, T. and Obermayer, K. (2000). Large Margin Rank Boundaries for Ordinal Regression. Advances in Large Margin Classifiers. MIT Press, 115 – 132.

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Lund, J. and Obermayer, K. (2002). Visual Cortex: Anatomical Structure and Models of Function. The Handbook of Brain Theory and Neural Networks. MIT Press, 1202 – 1205.

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

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Purwins, H., Graepel, T. and Obermayer, K. (2004). Correspondence Analysis of Pitch Class, Key, and Composer. Perspectives of Mathematical and Computational Music Theory. Epos-Verlag, 432 – 454.


Purwins, H., Normann, I. and Obermayer, K. (2005). Unendlichkeit - Konstruktion musikalischer Paradoxien. Mikrotöne und mehr: Auf György Ligetis Hamburger Pfaden. Bockel-Verlag, 39 – 80.


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.

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Adiloglu, K. and Obermayer, K. (2007). Topological Features of the Two-Voice Inventions. Communications in Computer and Information Science. Springer Berlin Heidelberg, 67 – 73.


Ochab, B., Neubauer, N. and Obermayer, K. (2008). Personalized Recommendations for the Web 3D. Lecture Notes in Computer Science. Springer Verlag, 374 – 377.

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Purwins, H., Blankertz, B. and Obermayer, K. (2008). Toroidal Models in Tonal Theory. Tonal Theory for the Digital Age - Computing in Musicology. Stanford University, 73 – 98.

Link zur Originalpublikation

Adiloglu, K., Annies, R., Henrich, F., Paus, A. and Obermayer, K. (2008). Geometrical Approaches to Active Learning. Autonomous Systems – Self-Organization, Management, and Control. Springer Netherlands, 11-19.

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Martin, R. and Obermayer, K. (2009). Theoretical and Computational Neuroscience: Self-Organizing Maps. The Encyclopedia of Neuroscience. Academic Press, 561 – 570.

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

Link zur Originalpublikation


Jain, B. and Obermayer, K. (2010). Elkan’s k-Means Algorithm for Graphs. Advances in Soft Computing. Springer Berlin Heidelberg, 22-32.

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Jain, B. and Obermayer, K. (2011). Maximum Likelihood for Gaussians on Graphs. Graph-Based Representations in Pattern Recognition. Springer Berlin Heidelberg, 62-71.

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