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Report (52)

481.
Report
Zhou, D.; Schölkopf, B.: Learning from Labeled and Unlabeled Data Using Random Walks. Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2004), 12 pp.
482.
Report
Lal, T.; Schröder, M.; Hinterberger, T.; Weston, J.; Bogdan , M.; Birbaumer, N.; Schölkopf, B.: Support Vector Channel Selection in BCI (Technical Report of the Max Planck Institute for Biological Cybernetics, 120). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2003), 9 pp.
483.
Report
Toyama, K.; Schölkopf, B.: Interactive Images. Microsoft Research, Redmond, WA, USA (2003), 7 pp.
484.
Report
Ham, J.; Lee, D.; Mika, S.; Schölkopf, B.: A kernel view of the dimensionality reduction of manifolds (Technical Report of the Max Planck Institute for Biological Cybernetics, 110). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2003), 8 pp.
485.
Report
Franz, M.; Schölkopf, B.: Implicit Wiener Series: Part I: Cross-Correlation vs. Regression in Reproducing Kernel Hilbert Spaces (Technical Report of the Max Planck Institute for Biological Cybernetics, 114). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2003), 15 pp.
486.
Report
Kim, K.; Franz, M.; Schölkopf, B.: Kernel Hebbian Algorithm for Iterative Kernel Principal Component Analysis (Technical Report of the Max Planck Institute for Biological Cybernetics, 109). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2003), 13 pp.
487.
Report
Zhou, D.; Bousquet, O.; Lal, T.; Weston, J.; Schölkopf, B.: Learning with Local and Global Consistency (Technical Report of the Max Planck Institute for Biological Cybernetics, 112). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2003), 8 pp.
488.
Report
Zhou, D.; Weston, J.; Gretton, A.; Bousquet, O.; Schölkopf, B.: Ranking on Data Manifolds (Technical Report of the Max Planck Institute for Biological Cybernetics, 113). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2003), 8 pp.
489.
Report
von Luxburg, U.; Bousquet, O.; Schölkopf, B.: A compression approach to support vector model selection (Technical Report of the Max Planck Institute for Biological Cybernetics, 101). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2002), 9 pp.
490.
Report
Weston, J.; Chapelle, O.; Elisseeff, A.; Schölkopf, B.; Vapnik, V.: Kernel Dependency Estimation (Technical Report of the Max Planck Institute for Biological Cybernetics, 98). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2002), 10 pp.
491.
Report
Romdhani, S.; Torr, P.; Schölkopf, B.; Blake, A.: Computationally Efficient Face Detection. Microsoft Research, Microsoft Corporation, Redmond, WA, USA (2002)
492.
Report
Weston, J.; Perez-Cruz F, Bousquet, O.; Chapelle, O.; Elisseeff, A.; Schölkopf, B.: Feature Selection and Transduction for Prediction of Molecular Bioactivity for Drug Design. (2002)
493.
Report
Bradshaw, B.; Schölkopf, B.; Platt, J.: Kernel Methods for Extracting Local Image Semantics. Microsoft Research, Microsoft Corporation, Redmond WA USA (2001), 9 pp.
494.
Report
Bartlett, P.; Schölkopf, B.: Some kernels for structured data. Biowulf Technologies, Savannah, GA, USA (2001)
495.
Report
Chapelle, O.; Schölkopf, B.: Incorporating Invariances in Non-Linear Support Vector Machines. (2001)
496.
Report
Chen, Y.; Fu, Q.; Gu, L.; Li, S.; Schölkopf, B.; Zhang, H.: Kernel Machine Based Learning for Multi-View Face Detection and Pose Estimation. Microsoft Research, Microsoft Corporation, Redmond, VA, USA (2001)
497.
Report
Gretton, A.; Herbrich R, Schölkopf, B.; Rayner, P.: Bound on the Leave-One-Out Error for 2-Class Classification using nu-SVMs. (2001)
498.
Report
Gretton, A.; Herbrich R, Schölkopf, B.; Smola, A.; Rayner, P.: Bound on the Leave-One-Out Error for Density Support Estimation using nu-SVMs. (2001)
499.
Report
Weston, J.; Elisseeff, A.; Schölkopf, B.: Use of the $ell_0$-norm with linear models and kernel methods. Biowulf Technologies, Savannah, GA, USA (2001)
500.
Report
Schölkopf, B.; Platt, J.; Shawe-Taylor, J.; Smola, A.; Williamson, R.: Estimating the support of a high-dimensional distribution. Microsoft Research, Microsoft Corporation, Redmond, VA, USA (2000), 30 pp.
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