Dr. Andreas Nienkötter

Research Associate
© Uni MS

Tel: +49 251 83 - 32706
a.nienkoetter(AT)uni-muenster.de

University of Münster
Department of Computer Science
Einsteinstrasse 62
D-48149 Münster

Office 602a
Consultation hour: by appointment

 
  • Research Foci

    • Generalized Median
    • Consensus Learning
  • CV

    Academic Education

    Bachelor and Master in Computer Science, WWU Münster

    WorkExperience

    Research Assistant at the Institute for Computer Science of the WWU Münster
  • Teaching

    • Computer Vision [100113]
      (in cooperation with Prof. Dr. Xiaoyi Jiang)

    • Pattern Recognition [108115]
      (in cooperation with Prof. Dr. Xiaoyi Jiang)

    • Computer Vision [106115]
      (in cooperation with Prof. Dr. Benjamin Risse)

    • Pattern Recognition [104112]
      (in cooperation with Prof. Dr. Benjamin Risse, Prof. Dr. Xiaoyi Jiang, Julian Johannes Kuhlmann)

    • Pattern Recognition [100123]
      (in cooperation with Prof. Dr. Benjamin Risse, Prof. Dr. Xiaoyi Jiang, Julian Johannes Kuhlmann)

  • Publications

    • , and . . “Kernel-based generalized median computation for consensus learning.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45 (5): 58725888.

    • , and . . “Distance-preserving vector space embedding for consensus learning.IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51 (2): 12441257.

    • , and . . “A lower bound for generalized median based consensus learning using kernel-induced distance functions.Pattern Recognition Letters, 140: 339347.

    • , and . . “Consensus learning for sequence data.” in Data Mining in Time Series and Streaming Databases, edited by M Last, H Bunke and A Kandel. Singapore: World Scientific Publishing.

    • , and . . “Distance-preserving vector space embedding for the closest string problem.” contribution to the Proc. of 23rd Int. Conf. on Pattern Recognition (ICPR), Cancun, Mexico doi: 10.1109/ICPR.2016.7899854.
    • , and . . “Improved prototype embedding based generalized median computation by means of refined reconstruction methods.” contribution to the Proc. of Joint IAPR Int. Workshop on Structural, Syntactic, and Statistical Pattern Recognition (S+SSPR), Merida, Mexico