Suchergebnisse

Poster (4)

21.
Poster
Seeger, M.; Nickisch, H.; Pohmann, R.; Schölkopf, B.: Optimization of k-Space Trajectories by Bayesian Experimental Design. 17th Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM 2009), Honolulu, HI, USA (2009)

Hochschulschrift - Doktorarbeit (1)

22.
Hochschulschrift - Doktorarbeit
Nickisch, H.: Bayesian Inference and Experimental Design for Large Generalised Linear Models. Dissertation, 158 S., Technische Universität Berlin, Berlin, Germany (2010)

Hochschulschrift - Diplom (1)

23.
Hochschulschrift - Diplom
Nickisch, H.: Extraction of visual features from natural video data using Slow Feature Analysis. Diplom, 100 S., Technische Universität Berlin, Berlin, Germany (2006)

Bericht (6)

24.
Bericht
Nickisch, H.; Seeger, M.: Multiple Kernel Learning: A Unifying Probabilistic Viewpoint. Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2011), 12 S.
25.
Bericht
Seeger, M.; Nickisch, H.: Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference. Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2010), 16 S.
26.
Bericht
Seeger, M.; Nickisch, H.: Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models. Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2010), 11 S.
27.
Bericht
Nickisch, H.; Rasmussen, C.: Gaussian Mixture Modeling with Gaussian Process Latent Variable Models. Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2010), 10 S.
28.
Bericht
Nickisch, H.; Kohli, P.; Rother, C.: Learning an Interactive Segmentation System. Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2009), 11 S.
29.
Bericht
Seeger, M.; Nickisch, H.: Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models (Technical Report of the Max Planck Institute for Biological Cybernetics, 175). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2008), 34 S.
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