Publications | Max Planck Institute for Intelligent Systems

Freifeld, O., Black, M. J.

Coregistration: Supplemental Material

Hirshberg, D., Loper, M., Rachlin, E., Black, M. J.

(No. 4), Max Planck Institute for Intelligent Systems, October 2012 (techreport)

pdf [BibTex]

ps Hirshberg, D., Loper, M., Rachlin, E., Black, M. J. Coregistration: Supplemental Material (No. 4), Max Planck Institute for Intelligent Systems, October 2012 (techreport)

pdf [BibTex]

Lie Bodies: A Manifold Representation of 3D Human Shape. Supplemental Material

(No. 5), Max Planck Institute for Intelligent Systems, October 2012 (techreport)

ps Freifeld, O., Black, M. J. Lie Bodies: A Manifold Representation of 3D Human Shape. Supplemental Material (No. 5), Max Planck Institute for Intelligent Systems, October 2012 (techreport)

MPI-Sintel Optical Flow Benchmark: Supplemental Material

Butler, D. J., Wulff, J., Stanley, G. B., Black, M. J.

(No. 6), Max Planck Institute for Intelligent Systems, October 2012 (techreport)

ps Butler, D. J., Wulff, J., Stanley, G. B., Black, M. J. MPI-Sintel Optical Flow Benchmark: Supplemental Material (No. 6), Max Planck Institute for Intelligent Systems, October 2012 (techreport)

Kernels for identifying patterns in datasets containing noise or transformation invariances

Schölkopf, B., Chapelle, C.

United States Patent, No. 8209269, June 2012 (patent)

ei Schölkopf, B., Chapelle, C. Kernels for identifying patterns in datasets containing noise or transformation invariances United States Patent, No. 8209269, June 2012 (patent)

Grosse-Wentrup, M., Schölkopf, B.

High Gamma-Power Predicts Performance in Brain-Computer Interfacing

(3), Max-Planck-Institut für Intelligente Systeme, Tübingen, February 2012 (techreport)

Abstract

Subjects operating a brain-computer interface (BCI) based on sensorimotor rhythms exhibit large variations in performance over the course of an experimental session. Here, we show that high-frequency gamma-oscillations, originating in fronto-parietal networks, predict such variations on a trial-to-trial basis. We interpret this nding as empirical support for an in uence of attentional networks on BCI-performance via modulation of the sensorimotor rhythm.

ei Grosse-Wentrup, M., Schölkopf, B. High Gamma-Power Predicts Performance in Brain-Computer Interfacing (3), Max-Planck-Institut für Intelligente Systeme, Tübingen, February 2012 (techreport)

HUMIM Software for Articulated Tracking

Soren Hauberg, Kim S. Pedersen

(01/2012), Department of Computer Science, University of Copenhagen, January 2012 (techreport)

Code PDF [BibTex]

ps Soren Hauberg, Kim S. Pedersen HUMIM Software for Articulated Tracking (01/2012), Department of Computer Science, University of Copenhagen, January 2012 (techreport)

Code PDF [BibTex]

A geometric framework for statistics on trees

Aasa Feragen, Mads Nielsen, Soren Hauberg, Pechin Lo, Marleen de Bruijne, Francois Lauze

(11/02), Department of Computer Science, University of Copenhagen, January 2012 (techreport)

ps Aasa Feragen, Mads Nielsen, Soren Hauberg, Pechin Lo, Marleen de Bruijne, Francois Lauze A geometric framework for statistics on trees (11/02), Department of Computer Science, University of Copenhagen, January 2012 (techreport)

2001

Kernel Methods for Extracting Local Image Semantics

Bradshaw, B., Schölkopf, B., Platt, J.

(MSR-TR-2001-99), Microsoft Research, October 2001 (techreport)

2001

ei Bradshaw, B., Schölkopf, B., Platt, J. Kernel Methods for Extracting Local Image Semantics (MSR-TR-2001-99), Microsoft Research, October 2001 (techreport)

Calibration of Digital Amateur Cameras

Urbanek, M., Horaud, R., Sturm, P.

(RR-4214), INRIA Rhone Alpes, Montbonnot, France, July 2001 (techreport)

ei Urbanek, M., Horaud, R., Sturm, P. Calibration of Digital Amateur Cameras (RR-4214), INRIA Rhone Alpes, Montbonnot, France, July 2001 (techreport)

Weston, J., Elisseeff, A., Schölkopf, B.

Use of the ell_0-norm with linear models and kernel methods

Biowulf Technologies, 2001 (techreport)

ei Weston, J., Elisseeff, A., Schölkopf, B. Use of the ell_0-norm with linear models and kernel methods Biowulf Technologies, 2001 (techreport)

KDD Cup 2001 data analysis: prediction of molecular bioactivity for drug design – Binding to Thrombin

Weston, J., Perez-Cruz, F., Bousquet, O., Chapelle, O., Elisseeff, A., Schölkopf, B.

BIOwulf, 2001 (techreport)

ei Weston, J., Perez-Cruz, F., Bousquet, O., Chapelle, O., Elisseeff, A., Schölkopf, B. KDD Cup 2001 data analysis: prediction of molecular bioactivity for drug design – Binding to Thrombin BIOwulf, 2001 (techreport)

Chapelle, O., Schölkopf, B.

Incorporating Invariances in Non-Linear Support Vector Machines

Max Planck Institute for Biological Cybernetics / Biowulf Technologies, 2001 (techreport)

Abstract

We consider the problem of how to incorporate in the Support Vector Machine (SVM) framework invariances given by some a priori known transformations under which the data should be invariant. It extends some previous work which was only applicable with linear SVMs and we show on a digit recognition task that the proposed approach is superior to the traditional Virtual Support Vector method.

ei Chapelle, O., Schölkopf, B. Incorporating Invariances in Non-Linear Support Vector Machines Max Planck Institute for Biological Cybernetics / Biowulf Technologies, 2001 (techreport)

Data cleaning algorithms with applications to micro-array experiments

Weston, J., Chapelle, O., Guyon, I.

Biowulf, 2001 (techreport)

ei Weston, J., Chapelle, O., Guyon, I. Data cleaning algorithms with applications to micro-array experiments Biowulf, 2001 (techreport)

Bound on the Leave-One-Out Error for Density Support Estimation using nu-SVMs

Gretton, A., Herbrich, R., Schölkopf, B., Smola, A., Rayner, P.

University of Cambridge, 2001 (techreport)

ei Gretton, A., Herbrich, R., Schölkopf, B., Smola, A., Rayner, P. Bound on the Leave-One-Out Error for Density Support Estimation using nu-SVMs University of Cambridge, 2001 (techreport)

Bound on the Leave-One-Out Error for 2-Class Classification using nu-SVMs

Gretton, A., Herbrich, R., Schölkopf, B., Rayner, P.

University of Cambridge, 2001, Updated May 2003 (literature review expanded) (techreport)

Abstract

Three estimates of the leave-one-out error for $nu$-support vector (SV) machine binary classifiers are presented. Two of the estimates are based on the geometrical concept of the {em span}, which was introduced in the context of bounding the leave-one-out error for $C$-SV machine binary classifiers, while the third is based on optimisation over the criterion used to train the $nu$-support vector classifier. It is shown that the estimates presented herein provide informative and efficient approximations of the generalisation behaviour, in both a toy example and benchmark data sets. The proof strategies in the $nu$-SV context are also compared with those used to derive leave-one-out error estimates in the $C$-SV case.