Publications | Max Planck Institute for Intelligent Systems

405 results (View BibTeX file of all listed publications)

2007

Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning

Rätsch, G., Sonnenburg, S., Srinivasan, J., Witte, H., Müller, K., Sommer, R., Schölkopf, B.

PLoS Computational Biology, 3(2, e20):0313-0322, February 2007 (article)

2007

ei Rätsch, G., Sonnenburg, S., Srinivasan, J., Witte, H., Müller, K., Sommer, R., Schölkopf, B. Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning PLoS Computational Biology, 3(2, e20):0313-0322, February 2007 (article)

PDF DOI [BibTex]

Quantum broadcasting problem in classical low-power signal processing

Janzing, D., Steudel, B.

Physical Review A, 75(2):11, February 2007 (article)

Web DOI [BibTex]

ei Janzing, D., Steudel, B. Quantum broadcasting problem in classical low-power signal processing Physical Review A, 75(2):11, February 2007 (article)

Web DOI [BibTex]

Statistical Consistency of Kernel Canonical Correlation Analysis

Fukumizu, K., Bach, F., Gretton, A.

Journal of Machine Learning Research, 8, pages: 361-383, February 2007 (article)

Abstract

While kernel canonical correlation analysis (CCA) has been applied in many contexts, the convergence of finite sample estimates of the associated functions to their population counterparts has not yet been established. This paper gives a mathematical proof of the statistical convergence of kernel CCA, providing a theoretical justification for the method. The proof uses covariance operators defined on reproducing kernel Hilbert spaces, and analyzes the convergence of their empirical estimates of finite rank to their population counterparts, which can have infinite rank. The result also gives a sufficient condition for convergence on the regularization coefficient involved in kernel CCA: this should decrease as n^{-1/3}, where n is the number of data.

PDF [BibTex]

ei Fukumizu, K., Bach, F., Gretton, A. Statistical Consistency of Kernel Canonical Correlation Analysis Journal of Machine Learning Research, 8, pages: 361-383, February 2007 (article)

PDF [BibTex]

Machine Learning for Mass Production and Industrial Engineering

Pfingsten, T.

Biologische Kybernetik, Eberhard-Karls-Universität Tübingen, Tübingen, Germany, February 2007 (phdthesis)

PDF [BibTex]

ei Pfingsten, T. Machine Learning for Mass Production and Industrial Engineering Biologische Kybernetik, Eberhard-Karls-Universität Tübingen, Tübingen, Germany, February 2007 (phdthesis)

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Some observations on the pedestal effect

Henning, G., Wichmann, F.

Journal of Vision, 7(1:3):1-15, January 2007 (article)

Abstract

The pedestal or dipper effect is the large improvement in the detectability of a sinusoidal grating observed when it is added to a masking or pedestal grating of the same spatial frequency, orientation, and phase. We measured the pedestal effect in both broadband and notched noiseVnoise from which a 1.5-octave band centered on the signal frequency had been removed. Although the pedestal effect persists in broadband noise, it almost disappears in the notched noise. Furthermore, the pedestal effect is substantial when either high- or low-pass masking noise is used. We conclude that the pedestal effect in the absence of notched noise results principally from the use of information derived from channels with peak sensitivities at spatial frequencies different from that of the signal and the pedestal. We speculate that the spatial-frequency components of the notched noise above and below the spatial frequency of the signal and the pedestal prevent off-frequency looking, that is, prevent the use of information about changes in contrast carried in channels tuned to spatial frequencies that are very much different from that of the signal and the pedestal. Thus, the pedestal or dipper effect measured without notched noise appears not to be a characteristic of individual spatial-frequency-tuned channels.

PDF Web DOI [BibTex]

ei Henning, G., Wichmann, F. Some observations on the pedestal effect Journal of Vision, 7(1:3):1-15, January 2007 (article)

PDF Web DOI [BibTex]

Development of a Brain-Computer Interface Approach Based on Covert Attention to Tactile Stimuli

Raths, C.

University of Tübingen, Germany, University of Tübingen, Germany, January 2007 (diplomathesis)

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