Publikationen von J Eichhorn

Eichhorn, J.; Sinz, F.; Bethge, M.: Natural Image Coding in V1: How Much Use is Orientation Selectivity? PLoS Computational Biology 5 (4), S. 1 - 16 (2009)

Laaksonen, J.; Larlus, D.; Schiele, B.; Everingham, M.; Zisserman, A.; Williams, C.; van Gool, L.; Allan, M.; Bishop, C.; Chapelle, O. et al.; Dalal, N.; Deselaers, T.; Dorkó, G.; Duffner, S.; Eichhorn, J.; Fritz, M.; Garcia, C.; Griffiths, T.; Jurie, F.; Keysers, T.; Koskela, M.; Leibe, B.; Meng, H.; Ney, H.; Schmid, C.; Seemann, E.; Shawe‐Taylor, J.; Storkey, A.; Szedmak, S.; Triggs, B.; Ulusoy, I.; Viitaniemi, V.; Jianguo, Z.: The 2005 PASCAL Visual Object Classes Challenge. In: Machine Learning Challenges: Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment First PASCAL Machine Learning Challenges Workshop, MLCW 2005, Southampton, UK, April 11-13, 2005, S. 117 - 176 (Hg. Quiñonero-Candela, J.; Dagan, I.; Magnini, B.; d’Alché-Buc, F.). First PASCAL Machine Learning Challenges Workshop (MLCW 2005), Southampton, UK, 11. April 2005 - 13. April 2005. Springer, Berlin (2006)

Eichhorn, J.; Tolias, A.; Zien, A.; Kuss, M.; Rasmussen, C.; Weston, J.; Logothetis, N.; Schölkopf, B.: Prediction on Spike Data Using Kernel Algorithms. In: Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference, S. 1367 - 1374 (Hg. Thrun, S.; Saul, L.; Schölkopf, B.). Seventeenth Annual Conference on Neural Information Processing Systems (NIPS 2003), Vancouver, BC, Canada, 08. Dezember 2003 - 13. Dezember 2003. MIT Press, Cambridge, MA, USA (2004)

Bethge, M.; Eichhorn, J.: Linking V1 receptive field properties to optimal coding principles. In 37th Annual Meeting of the Society for Neuroscience (Neuroscience 2007), 768.6. 37th Annual Meeting of the Society for Neuroscience (Neuroscience 2007), San Diego, CA, USA, 03. November 2007 - 07. November 2007. (2007)

Eichhorn, J.: Grundlagen von Support Vector Maschinen und Anwendungen in der Bildverarbeitung. Workshop: Numerical, Statistical and Discrete Methods in Image Processing, München, Germany (2004)

Eichhorn, J.: Applications of Kernel Machines to Structured Data. Dissertation, 157 S., Technische Universität Berlin, Berlin, Germany (2007)

Eichhorn, J.: Variationsverfahren zur Untersuchung von Grundzustandseigenschaften des Ein-Band Hubbard-Modells. Diplom, Technische Universität Dresden, Dresden, Germany (2001)

Bakır, G.; Wu, M.; Eichhorn, J.: Maximum-Margin Feature Combination for Detection and Categorization. Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2005), 8 S.

Eichhorn, J.; Chapelle, O.: Object categorization with SVM: kernels for local features (Technical Report of the Max Planck Institute for Biological Cybernetics, 137). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2004), 9 S.