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Machine Learning

Book Chapters

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 [4]. Künstliche Intelligenz. Springer Berlin Heidelberg, 353-362.

Link to original publication [5]

Böhmer, W. and Obermayer, K. (2015). Regression with Linear Factored Functions [6]. Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 119-134.

Link to publication [7]

2011

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.

Link to original publication [9]

Jain, B. and Obermayer, K. (2011). Maximum Likelihood for Gaussians on Graphs [10]. Graph-Based Representations in Pattern Recognition. Springer Berlin Heidelberg, 62-71.

Link to publication [11]

Jain, B. and Obermayer, K. (2011). Generalized Learning Graph Quantization [12]. Graph-Based Representations in Pattern Recognition. Springer Berlin Heidelberg, 122-131.

Link to publication [13]

2010

Jain, B. and Obermayer, K. (2010). Elkan’s k-Means Algorithm for Graphs [14]. Advances in Soft Computing. Springer Berlin Heidelberg, 22-32.

Link to publication [15]

2009

Jain, B. and Obermayer, K. (2009). Algorithms for the Sample Mean of Graphs [16]. Lecture Notes in Computer Science, 351 – 359.

Link to original publication [17]

2008

Adiloglu, K., Annies, R., Henrich, F., Paus, A. and Obermayer, K. (2008). Geometrical Approaches to Active Learning [18]. Autonomous Systems – Self-Organization, Management, and Control. Springer Netherlands, 11-19.

Link to publication [19]

2006

Hochreiter, S. and Obermayer, K. (2006). Nonlinear Feature Selection with the Potential Support Vector Machine [20]. Feature Extraction: Foundations and Applications. Springer Berlin Heidelberg, 419 – 438.

Link to original publication [21]

2000

Herbrich, R., Graepel, T. and Obermayer, K. (2000). Large Margin Rank Boundaries for Ordinal Regression [22]. Advances in Large Margin Classifiers. MIT Press, 115 – 132.

Link to publication [23]

1999

Hasenjäger, M., Ritter, H. and Obermayer, K. (1999). Active Learning in Self-Organizing Maps [24]. Kohonen Maps. Elsevier, 57–70.

Link to publication [25]

Herbrich, R., Keilbach, M., Graepel, T., Bollmann-Sdorra, P. and Obermayer, K. (1999). Neural Networks in Economics: Background, Applications and New Developments [26]. Advances in Computational Economics: Computational Techniques for Modelling Learning in Economics. Kluwer Academics, 169 – 196.

Link to publication [27]

1991

Ritter, H., Obermayer, K. and Rubner, J. (1991). Self-Organizing Maps and Adaptive Filters [28]. Physics of Neural Networks. Springer, 281 – 306.

Link to original publication [29]

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