Neural Networks with Fixed Binary Random Projections Improve Accuracy in Classifying Noisy Data

Yang Z, Schilling A, Maier A, Krauß P (2021)

Publication Type: Conference contribution

Publication year: 2021

Journal

Informatik aktuell Gesellschaft für Informatik (GI)

Publisher: Springer Science and Business Media Deutschland GmbH

Pages Range: 211-216

Conference Proceedings Title: Informatik aktuell

Event location: Regensburg DE

ISBN: 9783658331979

DOI: 10.1007/978-3-658-33198-6_51

Authors with CRIS profile

Zijin Yang Lehrstuhl für Informatik 5 (Mustererkennung) Achim Schilling Professur für Experimentelle HNO-Heilkunde Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung) Patrick Krauß Department of Otorhinolaryngology – Head and Neck Surgery (HNO-Klinik)

How to cite

APA:

Yang, Z., Schilling, A., Maier, A., & Krauß, P. (2021). Neural Networks with Fixed Binary Random Projections Improve Accuracy in Classifying Noisy Data. In Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 211-216). Regensburg, DE: Springer Science and Business Media Deutschland GmbH.

MLA:

Yang, Zijin, et al. "Neural Networks with Fixed Binary Random Projections Improve Accuracy in Classifying Noisy Data." Proceedings of the German Workshop on Medical Image Computing, 2021, Regensburg Ed. Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff, Springer Science and Business Media Deutschland GmbH, 2021. 211-216.

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