Christian IgelProfessor mso, Dr. habil.
|University of Copenhagen|
|2100 København Ø|
|Office:||HCØ - Building E, Office 4.0.2||Phone:||(+45) 21849673|
Short CVI studied Computer Science at the Technical University of Dortmund, Germany. In 2002, I received my Doctoral degree from the Faculty of Technology, Bielefeld University, Germany, and in 2010 my Habilitation degree from the Department of Electrical Engineering and Information Sciences, Ruhr-University Bochum, Germany. From 2003 to 2010, I was a Juniorprofessor for Optimization of Adaptive Systems at the Institut für Neuroinformatik, Ruhr-University Bochum. In October 2010, I was appointed professor with special duties in machine learning at DIKU.
Research InterestsMy main research area is Machine Learning.
Currently I am particularly interested in
- support vector machines and other kernel-based methods,
- evolution strategies for single- and multi-objective optimization,
- stochastic neural networks and undirected graphical models,
Selected PublicationsPlease click here for a full list. I also maintain a Google scholar profile.
Kai Brügge, Asja Fischer, and Christian Igel. The flip-the-state transition operator for restricted Boltzmann machines. Machine Learning
13, pp. 53-69, 2013
Oswin Krause, Asja Fischer, Tobias Glasmachers, and Christian Igel. Approximation properties of DBNs with binary hidden units and real-valued visible units. JMLR W&CP
28(1), pp. 419–426, 2013
Asja Fischer and Christian Igel. Bounding the Bias of Contrastive Divergence Learning. Neural Computation
23, pp. 664-673, 2011
Tobias Glasmachers and Christian Igel. Maximum Likelihood Model Selection for 1-Norm Soft Margin SVMs with Multiple Parameters. IEEE Transactions on Pattern Analysis and Machine Intelligence
32(8), pp. 1522-1528, 2010
Thorsten Suttorp, Nikolaus Hansen, and Christian Igel. Efficient Covariance Matrix Update for Variable Metric Evolution Strategies. Machine Learning
pp. 167-197, 2009 source code
Verena Heidrich-Meisner and Christian Igel. Hoeffding and Bernstein Races for Selecting Policies in Evolutionary Direct Policy Search. In L. Bottou and M. Littman, eds.: Proceedings of the International Conference on Machine Learning (ICML 2009), pp. 401-408, 2009
Christian Igel, Verena Heidrich-Meisner, and Tobias Glasmachers. Shark. Journal of Machine Learning Research
993-996, 2008 source code
Tobias Glasmachers and Christian Igel. Maximum-Gain Working Set Selection for SVMs. Journal of Machine Learning Research
7, pp. 1437-1466, 2006 source code