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Zeitschriftenartikel (7)

  1. 1.
    Zeitschriftenartikel
    Seeger, M.; Nickisch, H.: Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models. SIAM Journal on Imaging Sciences 4 (1), S. 166 - 199 (2011)
  2. 2.
    Zeitschriftenartikel
    Seeger, M.; Nickisch, H.; Pohmann, R.; Schölkopf, B.: Optimization of k-Space Trajectories for Compressed Sensing by Bayesian Experimental Design. Magnetic Resonance in Medicine 63 (1), S. 116 - 126 (2010)
  3. 3.
    Zeitschriftenartikel
    Nguyen-Tuong, D.; Seeger, M.; Peters, J.: Model Learning with Local Gaussian Process Regression. Advanced Robotics 23 (15), S. 2015 - 2034 (2009)
  4. 4.
    Zeitschriftenartikel
    Seeger, M.: Cross-validation Optimization for Large Scale Structured Classification Kernel Methods. The Journal of Machine Learning Research 9, S. 1147 - 1178 (2008)
  5. 5.
    Zeitschriftenartikel
    Seeger, M.; Kakade, S.; Foster, D.: Information Consistency of Nonparametric Gaussian Process Methods. IEEE Transactions on Information Theory 54 (5), S. 2376 - 2382 (2008)
  6. 6.
    Zeitschriftenartikel
    Seeger, M.: Bayesian Inference and Optimal Design for the Sparse Linear Model. Journal of Machine Learning Research 9, S. 759 - 813 (2008)
  7. 7.
    Zeitschriftenartikel
    Steinke, F.; Seeger, M.; Tsuda, K.: Experimental design for efficient identification of gene regulatory networks using sparse Bayesian models. BMC Systems Biology 1, 51, S. 1 - 15 (2007)

Buchkapitel (2)

  1. 8.
    Buchkapitel
    Nguyen-Tuong, D.; Seeger, M.; Peters, J.: Real-Time Local GP Model Learning. In: From Motor Learning to Interaction Learning in Robots, S. 193 - 207 (Hg. Sigaud, O.; Peters, J.). Springer, Berlin, Germany (2010)
  2. 9.
    Buchkapitel
    Seeger, M.: A Taxonomy for Semi-Supervised Learning Methods. In: Semi-Supervised Learning, S. 15 - 31 (Hg. Chapelle, O.; Schölkopf, B.; Zien, A.). MIT Press, Cambridge, MA, USA (2006)

Konferenzbeitrag (12)

  1. 10.
    Konferenzbeitrag
    Seeger, M.; Nickisch, H.: Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference. In: JMLR Workshop and Conference Proceedings, Bd. 15, S. 652 - 660 (Hg. Gordon, G.; Dunson, D.; Dudik, M.). 14th International Conference on Artificial Intelligence and Statistics (AISTATS 2011), Fort Lauderdale, FL, USA, 11. April 2011 - 13. April 2011. MIT Press, Cambridge, MA, USA (2011)
  2. 11.
    Konferenzbeitrag
    Nguyen-Tuong, D.; Seeger, M.; Peters, J.: Local Gaussian Process Regression for Real Time Online Model Learning and Control. In: Advances in neural information processing systems 21, S. 1193 - 1200 (Hg. Koller, D.; Schuurmans, D.; Bengio, Y.; Bottou, L.). Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008), Vancouver, BC, Canada, 08. Dezember 2008 - 10. Dezember 2008. Curran, Red Hook, NY, USA (2009)
  3. 12.
    Konferenzbeitrag
    Nickisch, H.; Seeger, M.: Convex variational Bayesian inference for large scale generalized linear models. In: ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning, S. 761 - 768 (Hg. Danyluk, A.; Bottou, L.; Littman, M.). 26th International Conference on Machine Learning, Montreal, Canada, 14. Juni 2009 - 18. Juni 2009. ACM Press, New York, NY, USA (2009)
  4. 13.
    Konferenzbeitrag
    Seeger, M.; Sra, S.; Cunningham, J.: Workshop summary: Numerical mathematics in machine learning. In: ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning, S. 1 - 9 (Hg. Danyluk, A.; Bottou, L.; Littman, M.). 26th Annual International Conference on Machine Learning (ICML 2009), Montreal, Quebec, Canada, 14. Juni 2009 - 18. Juni 2009. ACM Press, New York, NY, USA (2009)
  5. 14.
    Konferenzbeitrag
    Seeger, M.; Nickisch, H.; Pohmann, R.; Schölkopf, B.: Bayesian Experimental Design of Magnetic Resonance Imaging Sequences. In: Advances in neural information processing systems 21, S. 1441 - 1448 (Hg. Koller, D.; Schuurmans, D.; Bengio, Y.; Bottou, L.). Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008), Vancouver, BC, Canada, 08. Dezember 2008 - 10. Dezember 2008. Curran, Red Hook, NY, USA (2009)
  6. 15.
    Konferenzbeitrag
    Gerwinn, S.; Macke, J.; Seeger, M.; Bethge, M.: Bayesian Inference for Spiking Neuron Models with a Sparsity Prior. In: Advances in Neural Information Processing Systems 20: 21st Annual Conference on Neural Information Processing Systems 2007, S. 529 - 536 (Hg. Platt, C.; Koller, D.; Singer, Y.; Roweis, S.). Twenty-First Annual Conference on Neural Information Processing Systems (NIPS 2007), Vancouver, BC, Canada, 03. Dezember 2007 - 06. Dezember 2007. Curran, Red Hook, NY, USA (2008)
  7. 16.
    Konferenzbeitrag
    Seeger, M.; Nickisch, H.: Compressed Sensing and Bayesian Experimental Design. In: ICML '08: Proceedings of the 25th international conference on Machine, S. 912 - 919 (Hg. Cohen, W.; McCallum, A.; Roweis, S.). 25th International Conference on Machine Learning (ICML 2008), Helsinki, Finland, 05. Juli 2008 - 09. Juli 2008. ACM Press, New York, NY, USA (2008)
  8. 17.
    Konferenzbeitrag
    Nguyen-Tuong, D.; Seeger, M.; Peters, J.: Computed Torque Control with Nonparametric Regression Models. In: 2008 American Control Conference, S. 212 - 217. American Control Conference (ACC 2008), Seattle, WA, USA, 11. Juni 2008 - 13. Juni 2008. IEEE, Piscataway, NJ, USA (2008)
  9. 18.
    Konferenzbeitrag
    Nguyen-Tuong, D.; Peters, J.; Seeger, M.; Schölkopf, B.: Learning Inverse Dynamics: A Comparison. In: Advances in computational intelligence and learning: 16th European Symposium on Artificial Neural Networks, S. 13 - 18 (Hg. Verleysen, M.). 16th European Symposium on Artificial Neural Networks (ESANN 2008), Bruges, Belgium, 23. April 2008 - 25. April 2008. d-side, Evere, Belgium (2008)
  10. 19.
    Konferenzbeitrag
    Seeger, M.; Gerwinn, S.; Bethge, M.: Bayesian Inference for Sparse Generalized Linear Models. In: Machine Learning: ECML 2007: 18th European Conference on Machine Learning, Warsaw, Poland, September 17-21, 2007, S. 298 - 309 (Hg. Kok, N.; Koronacki, J.; Lopez de Mantaras, R.; Matwin, S.; Mladenic, D. et al.). 18th European Conference on Machine Learning (ECML 2007), Warsaw, Poland, 17. September 2007 - 21. September 2007. Springer, Berlin, Germany (2007)
  11. 20.
    Konferenzbeitrag
    Seeger, M.: Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods. In: Advances in Neural Information Processing Systems 19, S. 1233 - 1240 (Hg. Schölkopf, B.; Platt, J.; Hoffman, T.). Twentieth Annual Conference on Neural Information Processing Systems (NIPS 2006), Vancouver, BC, Canada, 04. Dezember 2006 - 07. Dezember 2006. MIT Press, Cambridge, MA, USA (2007)
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