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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.,10.1007/s13218-015-0356-1

Böhmer, W. and Obermayer, K. (2015). Regression with Linear Factored Functions [7]. Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 119-134.,10.1007/978-3-319-23528-8_8


Böhmer, W., Grünewälder, S., Nickisch, H. and Obermayer, K. (2011). Regularized Sparse Kernel Slow Feature Analysis [8]. Lecture Notes in Computer Science. Springer-Verlag Berlin Heidelberg, 235–248.,

Jain, B. and Obermayer, K. (2011). Maximum Likelihood for Gaussians on Graphs [9]. 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 [10]. 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 [11]. Advances in Soft Computing. Springer Berlin Heidelberg, 22-32.,10.1007/978-3-642-16773-7_2


Jain, B. and Obermayer, K. (2009). Algorithms for the Sample Mean of Graphs [12]. Lecture Notes in Computer Science, 351 – 359.,

Martin, R. and Obermayer, K. (2009). Theoretical and Computational Neuroscience: Self-Organizing Maps [13]. The Encyclopedia of Neuroscience. Academic Press, 561 – 570.,


Ochab, B., Neubauer, N. and Obermayer, K. (2008). Personalized Recommendations for the Web 3D [14]. Lecture Notes in Computer Science. Springer Verlag, 374 – 377.,http://dx.doi.org/10.1007/978-3-540-70987-9_57

Purwins, H., Blankertz, B. and Obermayer, K. (2008). Toroidal Models in Tonal Theory [15]. Tonal Theory for the Digital Age - Computing in Musicology. Stanford University, 73 – 98.,

Adiloglu, K., Annies, R., Henrich, F., Paus, A. and Obermayer, K. (2008). Geometrical Approaches to Active Learning [16]. Autonomous Systems – Self-Organization, Management, and Control. Springer Netherlands, 11-19.,10.1007/978-1-4020-8889-6_2


Adiloglu, K. and Obermayer, K. (2007). Topological Features of the Two-Voice Inventions [17]. Communications in Computer and Information Science. Springer Berlin Heidelberg, 67 – 73.,10.1007/978-3-642-04579-0_7


Hochreiter, S. and Obermayer, K. (2006). Nonlinear Feature Selection with the Potential Support Vector Machine [18]. Feature Extraction: Foundations and Applications. Springer Berlin Heidelberg, 419 – 438.,10.1007/978-3-540-35488-8_20


Purwins, H., Normann, I. and Obermayer, K. (2005). Unendlichkeit - Konstruktion musikalischer Paradoxien [19]. Mikrotöne und mehr: Auf György Ligetis Hamburger Pfaden. Bockel-Verlag, 39 – 80.,


Hochreiter, S. and Obermayer, K. (2004). Gene Selection for Microarray Data [20]. Kernel Methods in Computational Biology. MIT Press, 319 – 356.,

Purwins, H., Graepel, T. and Obermayer, K. (2004). Correspondence Analysis of Pitch Class, Key, and Composer [21]. 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 [22]. The Handbook of Brain Theory and Neural Networks. MIT Press, 1202 – 1205.,


Obermayer, K. (2000). Modeling the Formation of Sensory Representations in the Brain [23]. 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.,10.1007/978-94-010-0870-9_16

Stetter, M. and Obermayer, K. (2000). Biology and Theory of Early Vision in Mammals [24]. Brains and Biological Neural Networks. INNS Press, (1 – 50).,

Herbrich, R., Graepel, T. and Obermayer, K. (2000). Large Margin Rank Boundaries for Ordinal Regression [25]. Advances in Large Margin Classifiers. MIT Press, 115 – 132.,

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