Search results

  • Full text

  • Document type

    • Publication year

  • Organisation

Results: 10
Number of items: 10
  • Open Access
    Meertens, Q. A., Diks, C. G. H., van den Herik, H. J., & Takes, F. W. (2022). Improving the output quality of official statistics based on machine learning algorithms. Journal of Official Statistics, 22(2), 485-508. https://doi.org/10.2478/jos-2022-0023
  • Open Access
    Oostenbroek, M. H. W., van der Leij, M. J., Meertens, Q. A., Diks, C. G. H., & Wortelboer, H. M. (2021). Link-based influence maximization in networks of health promotion professionals. PLoS ONE, 16(8), Article e0256604. https://doi.org/10.1371/journal.pone.0256604
  • Open Access
    Meertens, Q. A. (2021). Misclassification bias in statistical learning. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Kloos, K., Meertens, Q., Scholtus, S., & Karch, J. (2021). Comparing correction methods to reduce misclassification bias. In M. Baratchi, L. Cao, W. A. Kosters, J. Lijffijt, J. N. van Rijn, & F. W. Takes (Eds.), Artificial Intelligence and Machine Learning: 32nd Benelux Conference, BNAIC/Benelearn 2020, Leiden, The Netherlands, November 19–20, 2020 : revised selected papers (pp. 64-90). (Communications in Computer and Information Science; Vol. 1398). Springer. https://doi.org/10.1007/978-3-030-76640-5_5
  • Meertens, Q. A., Diks, C., & Takes, F. (2020, December 7). Global business register used to estimate cross-border internet purchases within the EU [Data set]. Universiteit van Amsterdam. https://doi.org/10.21942/uva.13303082.v1
  • Meertens, Q. A. (2020, December 7). European tax returns used to estimate cross-border internet purchases within the EU [Data set]. Universiteit van Amsterdam. https://doi.org/10.21942/uva.13303034.v1
  • Open Access
    Meertens, Q. A., Diks, C. G. H., van den Herik, H. J., & Takes, F. W. (2020). A data‐driven supply‐side approach for estimating cross‐border Internet purchases within the European Union. Journal of the Royal Statistical Society. Series A (Statistics in Society), 183(1), 61-90. https://doi.org/10.1111/rssa.12487
  • Open Access
    Kloos, K., Meertens, Q., Scholtus, S., & Karch, J. (2020). Comparing correction methods to reduce misclassification bias. In L. Cao, W. Kosters, & J. Lijffijt (Eds.), BNAIC/BeNeLearn 2020: proceedings : Leiden, the Netherlands, November 19-20, 2020 (pp. 103-129). Universiteit Leiden. http://bnaic.liacs.leidenuniv.nl/bnaic2020proceedings.pdf
  • Open Access
    Burger, J., & Meertens, Q. (2020). The algorithm versus the chimps: On the minima of classifier performance metrics. In L. Cao, W. Kosters, & L. Lijffijt (Eds.), BNAIC/BeNeLearn 2020: proceedings : Leiden, the Netherlands, November 19-20, 2020 (pp. 38-55). Universiteit Leiden. https://bnaic.liacs.leidenuniv.nl/wordpress/wp-content/uploads/bnaic2020proceedings.pdf
  • Meertens, Q. A., Diks, C. G. H., van den Herik, H. J., & Takes, F. W. (2019). A Bayesian Approach for Accurate Classification-Based Aggregates. In T. Berger-Wolf, & N. Chawla (Eds.), Proceedings of the 2019 SIAM International Conference on Data Mining (pp. 306-314). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611975673.35
Page of