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Interaktive Visualisierung im Machine Learning Workflow

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Interaktive Datenvisualisierung in Wissenschaft und Unternehmenspraxis

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Zusammenfassung

In diesem Artikel wird eine Python-basierte Bibliothek für Visualisierungs- und Analysetechniken vorgestellt, die bei ETAS intern Anwendung finden wird. Diese soll Anwender bei der Bearbeitung von Machine-Learning-Fragestellungen unterstützen. Der Fokus liegt auf flexibel einsetzbaren, hochperformanten Techniken zur Analyse und für die Visualisierung von numerischen Datensätzen.

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Literatur

  1. Khronos Group Inc.: Opengl® 4.5 reference pages, (2019). [Online; Stand 20. August 2019]

    Google Scholar 

  2. Martin Abadi et al.: TensorFlow: Large-scale machine learning on heterogeneous systems. Software available from tensorflow.org. (2015)

    Google Scholar 

  3. Adam Paszke et al.: Automatic differentiation in pytorch. In NIPS-W, (2017)

    Google Scholar 

  4. Frangois Chollet et al.: Keras. GitHub. https://github.com/fchollet/keras (2015)

  5. Wes McKinney.: Data structures for statistical computing in python. In Stéfan van der Walt and Jarrod Millman, editors, Proceedings of the 9th Python in Science Conference, S. 51–56. (2010)

    Google Scholar 

  6. Pedregosa, F., et al.: Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  7. Hunter, J.D.: Matplotlib: A 2d graphics environment. Comput. Sci. Eng. 9(3), 90–95 (2007)

    Article  Google Scholar 

  8. Michael W.: Seaborn. https://doi.org/10.5281/zenodo.1313201

  9. Bokeh Development Team. Bokeh: Python library for interactive visualization. https://bokeh.org/ (2020)

  10. Jacob V.P. et al. Altair: Interactive statistical visualizations for python. J. Open Source Softw. 3(32), 1057 (2018)

    Google Scholar 

  11. Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugenics 7, 179–188 (1936)

    Article  Google Scholar 

  12. Dua, D., Graff, C.: UCI Machine Learning Repository, University of California, Irvine, School of Information and Computer Sciences. http://archive.ics.uci.edu/ml (2017)

  13. Ester, M., Kriegel, H-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, KDD’96, S. 226–231. AAAI Press, (1996)

    Google Scholar 

  14. Schubert, E., Sander, J., Ester, M., Kriegel, H.P., Xu, X.: DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN. Association for Computing Machinery 42(3) (2017)

    Google Scholar 

  15. Sam Hocevar.: Opengl tutorial. [Online; Stand 21. August 2019]

    Google Scholar 

  16. Sellers, G., Wright, R.S., Haemel, N.: OpenGL Superbible: Comprehensive Tutorial and Reference. Addison-Wesley Professional, 7. Aufl., (2015) ISBN 0672337479

    Google Scholar 

  17. Inselberg: The plane with parallel coordinates. Visual Comput. 1, 69–92 (1985)

    Google Scholar 

  18. Inselberg and B. Dimsdale Parallel coordinates: a tool for visualizing multi-dimensional geometry. In Proceedings of the First IEEE Conference on Visualization: Visualization 90, 361–378 (1990)

    Google Scholar 

  19. Julian Heinrich, Yuan Luo, Arthur E. Kirkpatrick, and Daniel Weiskopf: Evaluation of a bundling technique for parallel coordinates. Proceedings of the International Conference on Information Visualization Theory and Applications, (2012)

    Google Scholar 

  20. Ebert, D.S., Musgrave, F.K., Peachey, D., Perlin, K., Worley, S.: Texturing and modeling: a procedural approach. Morgan Kaufmann Publishers Inc., 3. Aufl., San Francisco, (2002)

    Google Scholar 

  21. Wikipedia: Kubisch Hermitescher Spline–Wikipedia, Die freie Enzyklopädie. (2019). [Online; Stand 20. August 2019]

    Google Scholar 

  22. Jolliffe, T.: Principal component analysis. Springer-Verlag, New York (2002)

    MATH  Google Scholar 

  23. van der Maaten, L., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. 9, 2579–2605 (2008)

    MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Machine-Learning-Gruppe der ETAS GmbH, Stuttgart, Deutschland

    Sebastian Boblest, Andrej Junginger & Thilo Strauss

Authors
  1. Sebastian Boblest
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  2. Andrej Junginger
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  3. Thilo Strauss
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Corresponding author

Correspondence to Sebastian Boblest .

Editor information

Editors and Affiliations

  1. Hochschule Rhein-Waal, Kamp-Lintfort, Germany

    Timo Kahl

  2. Hochschule Rhein-Waal, Kamp-Lintfort, Germany

    Frank Zimmer

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Boblest, S., Junginger, A., Strauss, T. (2020). Interaktive Visualisierung im Machine Learning Workflow. In: Kahl, T., Zimmer, F. (eds) Interaktive Datenvisualisierung in Wissenschaft und Unternehmenspraxis. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-29562-2_9

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