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2018


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Haptics and Haptic Interfaces

Kuchenbecker, K. J.

In Encyclopedia of Robotics, (Editors: Marcelo H. Ang and Oussama Khatib and Bruno Siciliano), Springer, May 2018 (incollection)

Abstract
Haptics is an interdisciplinary field that seeks to both understand and engineer touch-based interaction. Although a wide range of systems and applications are being investigated, haptics researchers often concentrate on perception and manipulation through the human hand. A haptic interface is a mechatronic system that modulates the physical interaction between a human and his or her tangible surroundings. Haptic interfaces typically involve mechanical, electrical, and computational layers that work together to sense user motions or forces, quickly process these inputs with other information, and physically respond by actuating elements of the user’s surroundings, thereby enabling him or her to act on and feel a remote and/or virtual environment.

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link (url) DOI [BibTex]

2018


link (url) DOI [BibTex]


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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)

ei

[BibTex]

[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)

ei

[BibTex]

[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)

ei

Project Page [BibTex]

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)

ei

[BibTex]

2016


[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)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]

2004


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Pattern detection methods and systems and face detection methods and systems

Blake, A., Romdhani, S., Schölkopf, B., Torr, P. H. S.

United States Patent, No 6804391, October 2004 (patent)

ei

[BibTex]

2004


[BibTex]


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Analysis of differential gene expression in healthy and osteoarthritic cartilage and isolated chondrocytes by microarray analysis

Aigner, T., Saas, J., Zien, A., Zimmer, R., Gebhard, P., Knorr, T.

In Volume 1: Cellular and Molecular Tools, pages: 109-128, (Editors: Sabatini, M., P. Pastoureau and F. De Ceuninck), Humana Press, July 2004 (inbook)

Abstract
The regulation of chondrocytes in osteoarthritic cartilage and the expression of specific gene products by these cells during early-onset and late-stage osteoarthritis are not well characterized. With the introduction of cDNA array technology, the measurement of thousands of different genes in one small tissue sample can be carried out. Interpretation of gene expression analyses in articular cartilage is aided by the fact that this tissue contains only one cell type in both normal and diseased conditions. However, care has to be taken not to over- and misinterpret results, and some major challenges must be overcome in order to utilize the potential of this technology properly in the field of osteoarthritis.

ei

Web [BibTex]

Web [BibTex]


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Distributed Command Execution

Stark, S., Berlin, M.

In BSD Hacks: 100 industrial-strength tips & tools, pages: 152-152, (Editors: Lavigne, Dru), O’Reilly, Beijing, May 2004 (inbook)

Abstract
Often you want to execute a command not only on one computer, but on several at once. For example, you might want to report the current statistics on a group of managed servers or update all of your web servers at once.

ei

[BibTex]

[BibTex]


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Gaussian Processes in Machine Learning

Rasmussen, CE.

In 3176, pages: 63-71, Lecture Notes in Computer Science, (Editors: Bousquet, O., U. von Luxburg and G. Rätsch), Springer, Heidelberg, 2004, Copyright by Springer (inbook)

Abstract
We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over functions. We present the simple equations for incorporating training data and examine how to learn the hyperparameters using the marginal likelihood. We explain the practical advantages of Gaussian Process and end with conclusions and a look at the current trends in GP work.

ei

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Local Alignment Kernels for Biological Sequences

Vert, J., Saigo, H., Akutsu, T.

In Kernel Methods in Computational Biology, pages: 131-153, MIT Press, Cambridge, MA,, 2004 (inbook)

ei

Web [BibTex]

Web [BibTex]


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Protein Classification via Kernel Matrix Completion

Kin, T., Kato, T., Tsuda, K.

In pages: 261-274, (Editors: Schoelkopf, B., K. Tsuda and J.P. Vert), MIT Press, Cambridge, MA; USA, 2004 (inbook)

ei

PDF [BibTex]

PDF [BibTex]


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Introduction to Statistical Learning Theory

Bousquet, O., Boucheron, S., Lugosi, G.

In Lecture Notes in Artificial Intelligence 3176, pages: 169-207, (Editors: Bousquet, O., U. von Luxburg and G. Rätsch), Springer, Heidelberg, Germany, 2004 (inbook)

ei

PDF [BibTex]

PDF [BibTex]


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A Primer on Kernel Methods

Vert, J., Tsuda, K., Schölkopf, B.

In Kernel Methods in Computational Biology, pages: 35-70, (Editors: B Schölkopf and K Tsuda and JP Vert), MIT Press, Cambridge, MA, USA, 2004 (inbook)

ei

PDF [BibTex]

PDF [BibTex]


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Concentration Inequalities

Boucheron, S., Lugosi, G., Bousquet, O.

In Lecture Notes in Artificial Intelligence 3176, pages: 208-240, (Editors: Bousquet, O., U. von Luxburg and G. Rätsch), Springer, Heidelberg, Germany, 2004 (inbook)

ei

PDF [BibTex]

PDF [BibTex]


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Kernels for graphs

Kashima, H., Tsuda, K., Inokuchi, A.

In pages: 155-170, (Editors: Schoelkopf, B., K. Tsuda and J.P. Vert), MIT Press, Cambridge, MA; USA, 2004 (inbook)

ei

PDF [BibTex]

PDF [BibTex]


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A primer on molecular biology

Zien, A.

In pages: 3-34, (Editors: Schoelkopf, B., K. Tsuda and J. P. Vert), MIT Press, Cambridge, MA, USA, 2004 (inbook)

Abstract
Modern molecular biology provides a rich source of challenging machine learning problems. This tutorial chapter aims to provide the necessary biological background knowledge required to communicate with biologists and to understand and properly formalize a number of most interesting problems in this application domain. The largest part of the chapter (its first section) is devoted to the cell as the basic unit of life. Four aspects of cells are reviewed in sequence: (1) the molecules that cells make use of (above all, proteins, RNA, and DNA); (2) the spatial organization of cells (``compartmentalization''); (3) the way cells produce proteins (``protein expression''); and (4) cellular communication and evolution (of cells and organisms). In the second section, an overview is provided of the most frequent measurement technologies, data types, and data sources. Finally, important open problems in the analysis of these data (bioinformatics challenges) are briefly outlined.

ei

PDF PostScript Web [BibTex]

PDF PostScript Web [BibTex]

1998


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Support-Vektor-Lernen

Schölkopf, B.

In Ausgezeichnete Informatikdissertationen 1997, pages: 135-150, (Editors: G Hotz and H Fiedler and P Gorny and W Grass and S Hölldobler and IO Kerner and R Reischuk), Teubner Verlag, Stuttgart, 1998 (inbook)

ei

[BibTex]

1998


[BibTex]