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Machine Learning

All Publications

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2014

Henniges, M., Turner, R. E., Sahani, M., Eggert, J. and Lücke, J. (2014). Efficient Occlusive Components Analysis [10]. Journal of Machine Learning Research, 15, 2689–2722.


2013

Böhmer, W., Grünewälder, S., Shen, Y., Musial, M. and Obermayer, K. (2013). Construction of Approximation Spaces for Reinforcement Learning [11]. Journal of Machine Learning Research, 14, 2067–2118.


Shen, Y., Stannat, W. and Obermayer, K. (2013). Risk-sensitive Markov Control Processes [12]. SIAM Journal on Control and Optimization, 51, 3652–3672.


Böhmer, W. and Obermayer, K. (2013). Towards Structural Generalization: Factored Approximate Planning [13]. ICRA Workshop on Autonomous Learning


2012

Böhmer, W., Grünewalder, S., Nickisch, H. and Obermayer, K. (2012). Generating feature spaces for linear algorithms with regularized sparse kernel slow feature analysis [14]. Machine Learning, 89, 67–86.


2011

Böhmer, W., Grünewälder, S., Nickisch, H. and Obermayer, K. (2011). Regularized Sparse Kernel Slow Feature Analysis [15]. Lecture Notes in Computer Science. Springer-Verlag Berlin Heidelberg, 235–248.,


Grünwälder, S. and Obermayer, K. (2011). The Optimal Unbiased Extimator and its Relation to LSTD, TD and MC [16]. Machine Learning, 83, 289 – 330.


Jain, B. and Obermayer, K. (2011). Maximum Likelihood for Gaussians on Graphs [17]. 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 [18]. Graph-Based Representations in Pattern Recognition. Springer Berlin Heidelberg, 122-131.,10.1007/978-3-642-20844-7_13


Jain, B. and Obermayer, K. (2011). Graph Quantization [19]. J. Comput. Vision Image Understanding, 115, 946–961.


Srinivasan, D. and Obermayer, K. (2011). Probabilistic prototype models for attributed graphs [20]. ,


Shen, Y., Grünewälder, S. and Obermayer, K. (2011). A Unified Framework for Risk-sensitive Markov Decision Processes with Finite State and Action Spaces [21]. ,


Jain, B. and Obermayer, K. (2011). Extending Bron Kerbosch for Solving the Maximum Weight Clique Problem [22]. ,


2010

Jain, B. and Obermayer, K. (2010). Elkan’s k-Means Algorithm for Graphs [23]. Advances in Soft Computing. Springer Berlin Heidelberg, 22-32.,10.1007/978-3-642-16773-7_2


Jain, B. and Obermayer, K. (2010). Consistent Estimator of Median and Mean Graph [24]. Proceedings of the 2010 20th International Conference on Pattern Recognition. IEEE, 1032–1035.,10.1109/ICPR.2010.258


Jain, B. and Obermayer, K. (2010). Large Sample Statistics in the Domain of Graphs [25]. Structural, Syntactic, and Statistical Pattern Recognition. Springer Berlin Heidelberg, 690 – 697.,10.1007/978-3-642-14980-1_10


Jain, B., Srinivasan, S. D., Tissen, A. and Obermayer, K. (2010). Learning Graph Quantization [26]. Structural, Syntactic, and Statistical Pattern Recognition. Springer Berlin Heidelberg, 109 – 118.,10.1007/978-3-642-14980-1_10


Jain, B. and Obermayer, K. (2010). Accelerating Competetive Learning Graph Quantization [27]. ,


2009

Jain, B. and Obermayer, K. (2009). Algorithms for the Sample Mean of Graphs [28]. Lecture Notes in Computer Science, 351 – 359.,


Jain, B. and Obermayer, K. (2009). Structure Spaces [29]. Journal of Machine Learning Research, 10, 2667 – 2714.


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