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TU Berlin

Inhalt des Dokuments

Analyse neuronaler Daten

Buchkapitel

2015

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. Künstliche Intelligenz. Springer Berlin Heidelberg, 353-362.

Link zur Originalpublikation

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

Link zur Publikation

2011

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

Link zur Originalpublikation

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

Link zur Publikation

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

Link zur Publikation

2010

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

Link zur Publikation

2009

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

Link zur Originalpublikation

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


2008

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

Link zur Publikation

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.

Link zur Publikation

2007

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


2006

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.

Link zur Originalpublikation

2005

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


2004

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

Link zur Publikation

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.


2002

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.

Link zur Publikation

2000

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).

Link zur Publikation

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

Link zur Publikation

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