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Conference Paper

J. Neumann, Schnörr, C., and Steidl, G., “Feasible Adaption Criteria for Hybrid Wavelet – Large Margin Classifiers”, in Proc. Computer Analysis of Images and Patterns (CAIP'03), 2003, vol. 2756, pp. 588–595.

J. Neumann, Schnörr, C., and Steidl, G., “SVM-based Feature Selection by Direct Objective Minimisation”, in Pattern Recognition, Proc. 26th DAGM Symposium, 2004, vol. 3175, pp. 212-219.

Journal Article

J. Neumann, Schnörr, C., and Steidl, G., “Combined SVM-based Feature Selection and Classification”, Machine Learning, vol. 61, pp. 129-150, 2005.

J. Neumann, Schnörr, C., and Steidl, G., “Efficient Wavelet Adaption for Hybrid Wavelet-Large Margin Classifiers”, Pattern Recognition, vol. 38, pp. 1815-1830, 2005.

J. Yuan, Schnörr, C., and Steidl, G., “Simultaneous Optical Flow Estimation and Decomposition”, SIAM J.~Scientific Computing, vol. 29, pp. 2283-2304, 2007.

Technical Report (1.16 MB)

J. Yuan, Schnörr, C., and Steidl, G., “Simultaneous Optical Flow Estimation and Decomposition”, SIAM J. Scientific Computing, vol. 29, pp. 2283-2304, 2007.

Techreport

J. Neumann, Schnörr, C., and Steidl, G., “Effectively Finding the Optimal Wavelet for Hybrid Wavelet - Large Margin Signal Classification”, Dept. Math. and Comp. Science, University of Mannheim, Germany, 5, 2003.