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Inhalt des Dokuments

Analyse neuronaler Daten


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Böhmer, W., Springenberg, J. T., Boedecker, J., Riedmiller, M. and Obermayer, K. (2015). Autonomous Learning of State Representations for Control: An Emerging Field Aims to Autonomously Learn State Representations for Reinforcement Learning Agents from Their Real-World Sensor Observations [6]. Künstliche Intelligenz. Springer Berlin Heidelberg, 353-362.

Link zur Originalpublikation [7]

Böhmer, W. and Obermayer, K. (2015). Regression with Linear Factored Functions [8]. Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 119-134.

Link zur Publikation [9]


Böhmer, W., Grünewälder, S., Nickisch, H. and Obermayer, K. (2011). Regularized Sparse Kernel Slow Feature Analysis [10]. Lecture Notes in Computer Science. Springer-Verlag Berlin Heidelberg, 235–248.

Link zur Originalpublikation [11]

Jain, B. and Obermayer, K. (2011). Maximum Likelihood for Gaussians on Graphs [12]. Graph-Based Representations in Pattern Recognition. Springer Berlin Heidelberg, 62-71.

Link zur Publikation [13]

Jain, B. and Obermayer, K. (2011). Generalized Learning Graph Quantization [14]. Graph-Based Representations in Pattern Recognition. Springer Berlin Heidelberg, 122-131.

Link zur Publikation [15]


Jain, B. and Obermayer, K. (2010). Elkan’s k-Means Algorithm for Graphs [16]. Advances in Soft Computing. Springer Berlin Heidelberg, 22-32.

Link zur Publikation [17]


Jain, B. and Obermayer, K. (2009). Algorithms for the Sample Mean of Graphs [18]. Lecture Notes in Computer Science, 351 – 359.

Link zur Originalpublikation [19]

Martin, R. and Obermayer, K. (2009). Theoretical and Computational Neuroscience: Self-Organizing Maps [20]. The Encyclopedia of Neuroscience. Academic Press, 561 – 570.


Ochab, B., Neubauer, N. and Obermayer, K. (2008). Personalized Recommendations for the Web 3D [21]. Lecture Notes in Computer Science. Springer Verlag, 374 – 377.

Link zur Publikation [22]

Purwins, H., Blankertz, B. and Obermayer, K. (2008). Toroidal Models in Tonal Theory [23]. Tonal Theory for the Digital Age - Computing in Musicology. Stanford University, 73 – 98.

Link zur Originalpublikation [24]

Adiloglu, K., Annies, R., Henrich, F., Paus, A. and Obermayer, K. (2008). Geometrical Approaches to Active Learning [25]. Autonomous Systems – Self-Organization, Management, and Control. Springer Netherlands, 11-19.

Link zur Publikation [26]


Adiloglu, K. and Obermayer, K. (2007). Topological Features of the Two-Voice Inventions [27]. Communications in Computer and Information Science. Springer Berlin Heidelberg, 67 – 73.


Hochreiter, S. and Obermayer, K. (2006). Nonlinear Feature Selection with the Potential Support Vector Machine [28]. Feature Extraction: Foundations and Applications. Springer Berlin Heidelberg, 419 – 438.

Link zur Originalpublikation [29]


Purwins, H., Normann, I. and Obermayer, K. (2005). Unendlichkeit - Konstruktion musikalischer Paradoxien [30]. Mikrotöne und mehr: Auf György Ligetis Hamburger Pfaden. Bockel-Verlag, 39 – 80.


Hochreiter, S. and Obermayer, K. (2004). Gene Selection for Microarray Data [31]. Kernel Methods in Computational Biology. MIT Press, 319 – 356.

Link zur Publikation [32]

Purwins, H., Graepel, T. and Obermayer, K. (2004). Correspondence Analysis of Pitch Class, Key, and Composer [33]. Perspectives of Mathematical and Computational Music Theory. Epos-Verlag, 432 – 454.


Lund, J. and Obermayer, K. (2002). Visual Cortex: Anatomical Structure and Models of Function [34]. The Handbook of Brain Theory and Neural Networks. MIT Press, 1202 – 1205.

Link zur Publikation [35]


Obermayer, K. (2000). Modeling the Formation of Sensory Representations in the Brain [36]. 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 [37]

Stetter, M. and Obermayer, K. (2000). Biology and Theory of Early Vision in Mammals [38]. Brains and Biological Neural Networks. INNS Press, (1 – 50).

Link zur Publikation [39]

Herbrich, R., Graepel, T. and Obermayer, K. (2000). Large Margin Rank Boundaries for Ordinal Regression [40]. Advances in Large Margin Classifiers. MIT Press, 115 – 132.

Link zur Publikation [41]

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