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  2. Spenader, J., & Roest, C. (2019). Facilitating Quantifier Acquisition: Training Can Eliminate Children's Spreading Errors. In BUCLD 43: Proceedings of the 43rd annual Boston University Conference on Language Development edited by Megan M. Brown and Brady Dailey (Vol. 2, pp. 653-666). Boston, USA: Cascadilla Press.
  3. Bakker, J., & Bunte, K. (2019). Efficient learning of email similarities for customer support. In M. Verleysen (Ed.), 27th European Symposium on Artificial Neural Networks, ESANN 2019 (pp. 119-124). d-side publishing.
  4. Biehl, M., Caticha, N., Opper, M., & Villmann, T. (2019). Statistical Physics of Learning and Inference. In M. Verleysen (Ed.), Proc. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning : ESANN 2019 Ciaco - i6doc.com.
  5. Biehl, M. (2019). Supervised Learning - An Introduction: Lectures given at the 30th Canary Islands Winter School of Astrophysics. (Machine Learning Reports; Vol. 01/2019). Mittweida, Germany: Machine Learning Reports.
  6. Biehl, M., Abadi, F., Göpfert, C., & Hammer, B. (2019). Prototype-based classifiers in the presence of concept drift: A modelling framework. ArXiv e-prints, 1903.07273 (1903.07273 ), [1903.07273 ].
  7. Wolf, B., & van Netten, S. (2019). Training submerged source detection for a 2D fluid flow sensor array with Extreme Learning Machines. In Eleventh International Conference on Machine Vision (ICMV 2018) (Vol. 11041, pp. 1104126). SPIE.Digital Library. https://doi.org/10.1117/12.2522667
  8. You, J., Trager, S., & Wilkinson, M. H. F. (Accepted/In press). A Fast, Memory-Efficient Alpha-Tree Algorithm using Flooding and Tree Size Estimation. Paper presented at International Symposium on Mathematical Morphology, Saarbrücken, Germany.
  9. Ayoobi, H., Cao, M., Verbrugge, L., & Verheij, B. (2019). Handling Unforeseen Failures Using Argumentation-Based Learning. Manuscript submitted for publication. In International Conference on Automation Science and Engineering (CASE) 2019 (pp. 1-8)
  10. Kuijken, K., Heymans, C., Dvornik, A., Hildebrandt, H., de Jong, J. T. A., Wright, A. H., ... Verdoes Kleijn, G. A. (2019). The fourth data release of the Kilo-Degree Survey: ugri imaging and nine-band optical-IR photometry over 1000 square degrees. Manuscript submitted for publication.
  11. Costa, A. C., Barufaldi, B., Borges, L. R., Biehl, M., Maidment, A. D. A., & Vieira, M. A. C. (2019). Analysis of feature relevance using an image quality index applied to digital mammography. In SPIE Medical Imaging 2019 (Vol. 10948). [109485R] San Diego, CA, USA: Society of Photo-Optical Instrumentation Engineers (SPIE). https://doi.org/10.1117/12.2512975
  12. Pfannschmidt, L., Jakob, J., Biehl, M., Tino, P., & Hammer, B. (2019). Feature Relevance Bounds for Ordinal Regression. ArXiv e-prints.
  13. Marlevi, D., Ruijsink, B., Balmus, M., Dillon-Murphy, D., Fovargue, D., Pushparajah, K., ... Nordsletten, D. A. (2019). Estimation of Cardiovascular Relative Pressure Using Virtual Work-Energy. Scientific Reports, 9, [1375]. https://doi.org/10.1038/s41598-018-37714-0
  14. Vermeeren, M., Bravetti, A., & Seri, M. (2019). Contact variational integrators. Manuscript submitted for publication.
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