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10 results (View BibTeX file of all listed publications)

2018

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Maschinelles Lernen: Entwicklung ohne Grenzen?

Schökopf, B.

In Mit Optimismus in die Zukunft schauen. Künstliche Intelligenz - Chancen und Rahmenbedingungen, pages: 26-34, (Editors: Bender, G. and Herbrich, R. and Siebenhaar, K.), B&S Siebenhaar Verlag, 2018 (incollection)

[BibTex]

2018

Schökopf, B. Maschinelles Lernen: Entwicklung ohne Grenzen? In Mit Optimismus in die Zukunft schauen. Künstliche Intelligenz - Chancen und Rahmenbedingungen, pages: 26-34, (Editors: Bender, G. and Herbrich, R. and Siebenhaar, K.), B&S Siebenhaar Verlag, 2018 (incollection)

[BibTex]

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Methods in Psychophysics

Wichmann, F. A., Jäkel, F.

In Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience, 5 (Methodology), 7, 4th, John Wiley & Sons, Inc., 2018 (inbook)

[BibTex]

Wichmann, F. A., Jäkel, F. Methods in Psychophysics In Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience, 5 (Methodology), 7, 4th, John Wiley & Sons, Inc., 2018 (inbook)

[BibTex]

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Transfer Learning for BCIs

Jayaram, V., Fiebig, K., Peters, J., Grosse-Wentrup, M.

In Brain–Computer Interfaces Handbook, pages: 425-442, 22, (Editors: Chang S. Nam, Anton Nijholt and Fabien Lotte), CRC Press, 2018 (incollection)

Project Page [BibTex]

Jayaram, V., Fiebig, K., Peters, J., Grosse-Wentrup, M. Transfer Learning for BCIs In Brain–Computer Interfaces Handbook, pages: 425-442, 22, (Editors: Chang S. Nam, Anton Nijholt and Fabien Lotte), CRC Press, 2018 (incollection)

Project Page [BibTex]

2016

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Nonlinear functional causal models for distinguishing cause from effect

Zhang, K., Hyvärinen, A.

In Statistics and Causality: Methods for Applied Empirical Research, pages: 185-201, 8, 1st, (Editors: Wolfgang Wiedermann and Alexander von Eye), John Wiley & Sons, Inc., 2016 (inbook)

[BibTex]

2016

Zhang, K., Hyvärinen, A. Nonlinear functional causal models for distinguishing cause from effect In Statistics and Causality: Methods for Applied Empirical Research, pages: 185-201, 8, 1st, (Editors: Wolfgang Wiedermann and Alexander von Eye), John Wiley & Sons, Inc., 2016 (inbook)

[BibTex]

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A cognitive brain–computer interface for patients with amyotrophic lateral sclerosis

Hohmann, M., Fomina, T., Jayaram, V., Widmann, N., Förster, C., Just, J., Synofzik, M., Schölkopf, B., Schöls, L., Grosse-Wentrup, M.

In Brain-Computer Interfaces: Lab Experiments to Real-World Applications, 228(Supplement C):221-239, 8, Progress in Brain Research, (Editors: Damien Coyle), Elsevier, 2016 (incollection)

DOI Project Page [BibTex]

Hohmann, M., Fomina, T., Jayaram, V., Widmann, N., Förster, C., Just, J., Synofzik, M., Schölkopf, B., Schöls, L., Grosse-Wentrup, M. A cognitive brain–computer interface for patients with amyotrophic lateral sclerosis In Brain-Computer Interfaces: Lab Experiments to Real-World Applications, 228(Supplement C):221-239, 8, Progress in Brain Research, (Editors: Damien Coyle), Elsevier, 2016 (incollection)

DOI Project Page [BibTex]

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Screening Rules for Convex Problems

Raj, A., Olbrich, J., Gärtner, B., Schölkopf, B., Jaggi, M.

2016 (unpublished) Submitted

[BibTex]

Raj, A., Olbrich, J., Gärtner, B., Schölkopf, B., Jaggi, M. Screening Rules for Convex Problems 2016 (unpublished) Submitted

[BibTex]

2008

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New Frontiers in Characterizing Structure and Dynamics by NMR

Nilges, M., Markwick, P., Malliavin, TE., Rieping, W., Habeck, M.

In Computational Structural Biology: Methods and Applications, pages: 655-680, (Editors: Schwede, T. , M. C. Peitsch), World Scientific, New Jersey, NJ, USA, May 2008 (inbook)

Abstract
Nuclear Magnetic Resonance (NMR) spectroscopy has emerged as the method of choice for studying both the structure and the dynamics of biological macromolecule in solution. Despite the maturity of the NMR method for structure determination, its application faces a number of challenges. The method is limited to systems of relatively small molecular mass, data collection times are long, data analysis remains a lengthy procedure, and it is difficult to evaluate the quality of the final structures. The last years have seen significant advances in experimental techniques to overcome or reduce some limitations. The function of bio-macromolecules is determined by both their 3D structure and conformational dynamics. These molecules are inherently flexible systems displaying a broad range of dynamics on time–scales from picoseconds to seconds. NMR is unique in its ability to obtain dynamic information on an atomic scale. The experimental information on structure and dynamics is intricately mixed. It is however difficult to unite both structural and dynamical information into one consistent model, and protocols for the determination of structure and dynamics are performed independently. This chapter deals with the challenges posed by the interpretation of NMR data on structure and dynamics. We will first relate the standard structure calculation methods to Bayesian probability theory. We will then briefly describe the advantages of a fully Bayesian treatment of structure calculation. Then, we will illustrate the advantages of using Bayesian reasoning at least partly in standard structure calculations. The final part will be devoted to interpretation of experimental data on dynamics.

Web [BibTex]

2008

Nilges, M., Markwick, P., Malliavin, TE., Rieping, W., Habeck, M. New Frontiers in Characterizing Structure and Dynamics by NMR In Computational Structural Biology: Methods and Applications, pages: 655-680, (Editors: Schwede, T. , M. C. Peitsch), World Scientific, New Jersey, NJ, USA, May 2008 (inbook)

Web [BibTex]

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A Robot System for Biomimetic Navigation: From Snapshots to Metric Embeddings of View Graphs

Franz, MO., Stürzl, W., Reichardt, W., Mallot, HA.

In Robotics and Cognitive Approaches to Spatial Mapping, pages: 297-314, Springer Tracts in Advanced Robotics ; 38, (Editors: Jefferies, M.E. , W.-K. Yeap), Springer, Berlin, Germany, 2008 (inbook)

Abstract
Complex navigation behaviour (way-finding) involves recognizing several places and encoding a spatial relationship between them. Way-finding skills can be classified into a hierarchy according to the complexity of the tasks that can be performed [8]. The most basic form of way-finding is route navigation, followed by topological navigation where several routes are integrated into a graph-like representation. The highest level, survey navigation, is reached when this graph can be embedded into a common reference frame. In this chapter, we present the building blocks for a biomimetic robot navigation system that encompasses all levels of this hierarchy. As a local navigation method, we use scene-based homing. In this scheme, a goal location is characterized either by a panoramic snapshot of the light intensities as seen from the place, or by a record of the distances to the surrounding objects. The goal is found by moving in the direction that minimizes the discrepancy between the recorded intensities or distances and the current sensory input. For learning routes, the robot selects distinct views during exploration that are close enough to be reached by snapshot-based homing. When it encounters already visited places during route learning, it connects the routes and thus forms a topological representation of its environment termed a view graph. The final stage, survey navigation, is achieved by a graph embedding procedure which complements the topologic information of the view graph with odometric position estimates. Calculation of the graph embedding is done with a modified multidimensional scaling algorithm which makes use of distances and angles between nodes.

PDF PDF DOI [BibTex]

Franz, MO., Stürzl, W., Reichardt, W., Mallot, HA. A Robot System for Biomimetic Navigation: From Snapshots to Metric Embeddings of View Graphs In Robotics and Cognitive Approaches to Spatial Mapping, pages: 297-314, Springer Tracts in Advanced Robotics ; 38, (Editors: Jefferies, M.E. , W.-K. Yeap), Springer, Berlin, Germany, 2008 (inbook)

PDF PDF DOI [BibTex]

1999

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Kernel principal component analysis.

Schölkopf, B., Smola, A., Müller, K.

In Advances in Kernel Methods—Support Vector Learning, pages: 327-352, (Editors: B Schölkopf and CJC Burges and AJ Smola), MIT Press, Cambridge, MA, 1999 (inbook)

[BibTex]

1999

Schölkopf, B., Smola, A., Müller, K. Kernel principal component analysis. In Advances in Kernel Methods—Support Vector Learning, pages: 327-352, (Editors: B Schölkopf and CJC Burges and AJ Smola), MIT Press, Cambridge, MA, 1999 (inbook)

[BibTex]

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Entropy numbers, operators and support vector kernels.

Williamson, R., Smola, A., Schölkopf, B.

In Advances in Kernel Methods - Support Vector Learning, pages: 127-144, (Editors: B Schölkopf and CJC Burges and AJ Smola), MIT Press, Cambridge, MA, 1999 (inbook)

[BibTex]

Williamson, R., Smola, A., Schölkopf, B. Entropy numbers, operators and support vector kernels. In Advances in Kernel Methods - Support Vector Learning, pages: 127-144, (Editors: B Schölkopf and CJC Burges and AJ Smola), MIT Press, Cambridge, MA, 1999 (inbook)

[BibTex]