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TU Berlin

Inhalt des Dokuments

Analyse neuronaler Daten

Buchkapitel

A

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

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


B

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

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

H

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

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

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

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

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

Link zur Publikation

J

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

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

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

Link zur Originalpublikation

L

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

M

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

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


O

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

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

P

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

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