1. 2018
  2. A benchmark and comparison of active learning for logistic regression

    Yang, Y. & Loog, M., 2018, In : Pattern Recognition. 83, p. 401-415 15 p.

    Research output: Contribution to journalArticleScientificpeer-review

  3. A variance maximization criterion for active learning

    Yang, Y. & Loog, M., 2018, In : Pattern Recognition. 78, p. 358-370 13 p.

    Research output: Contribution to journalArticleScientificpeer-review

  4. Artificial Empathic Memory: Enabling Media Technologies to Better Understand Subjective User Experience

    Dudzik, B., Hung, H., Neerincx, M. & Broekens, J., 2018, Proceedings of the 2018 Workshop on Understanding Subjective Attributes of Data, with the Focus on Evoked Emotions, EE-USAD 2018. New York, NY: Association for Computing Machinery (ACM), p. 1-8 8 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  5. Asymmetric kernel in Gaussian Processes for learning target variance

    Pintea, S. L., van Gemert, J. C. & Smeulders, A. W. M., 2018, In : Pattern Recognition Letters. 108, p. 70-77 8 p.

    Research output: Contribution to journalArticleScientificpeer-review

  6. Automatic quantification of bone marrow edema on MRI of the wrist in patients with early arthritis: A feasibility study

    Aizenberg, E., Roex, E. A. H., Nieuwenhuis, W. P., Mangnus, L., van der Helm-Mil, A. H. M., Reijnierse, M., Bloem, J. L., Lelieveldt, B. P. F. & Stoel, B. G., 2018, In : Magnetic Resonance in Medicine. 79, 2, p. 1127-1134 8 p.

    Research output: Contribution to journalArticleScientificpeer-review

  7. Bioinformatic Analysis of Genomic and Transcriptomic Variation in Fungi

    Gehrmann, T., 2018, 121 p.

    Research output: ThesisDissertation (TU Delft)Scientific

  8. Capturing human behaviour through wearables by computational analysis of social dynamics

    Gedik, E., 2018, 160 p.

    Research output: ThesisDissertation (TU Delft)Scientific

  9. Contextual loss functions for optimization of convolutional neural networks generating pseudo CTs from MRI

    van Stralen, M., Zhou, Y., Wozny, P. J., Seevinck, P. R. & Loog, M., 2018, Medical Imaging 2018: Image Processing. Angelini, E. D. & Landman, B. A. (eds.). Bellingham: SPIE, p. 105741N-1 - 105741N-6 6 p. 105741N. (Proceedings of Spie; vol. 10574).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  10. CyteGuide: Visual Guidance for Hierarchical Single-Cell Analysis

    Hollt, T., Pezzotti, N., van Unen, V., Koning, F., Lelieveldt, B. P. F. & Vilanova, A., 2018, In : IEEE Transactions on Visualization and Computer Graphics. 24, 1, p. 739-748 10 p., 8017575.

    Research output: Contribution to journalArticleScientificpeer-review

  11. DeepEyes: Progressive Visual Analytics for Designing Deep Neural Networks

    Pezzotti, N., Hollt, T., van Gemert, J., Lelieveldt, B., Eisemann, E. & Vilanova Bartroli, A., 2018, In : IEEE Transactions on Visualization and Computer Graphics. 24, 1, p. 98-108 11 p., 8019872.

    Research output: Contribution to journalArticleScientificpeer-review

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