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Results: 118
Number of items: 118
  • Open Access
    Winkels, M., Roijers, D. M., van Someren, M., Yamamoto, E., Pronk, R., Odijk, E., & de Jonge, M. (2018). Challenge Balancing for a Kanji E-Tutoring System. In M. Atzmueller, & W. Duivesteijn (Eds.), 30th Benelux Conference on Artificial Intelligence: BNAIC 2018 Preproceedings : November 8-9, 2018, Jheronimus Academy of Data Science (JADS), 's-Hertogenbosch, The Netherlands (pp. 331-340). (BNAIC; Vol. 30). Jheronimus Academy of Data Science.
  • Open Access
    van der Meulen, A., Kwisthout, J., ten Teije, A., Schlobach, S., van Splunter, S., Winands, M., van Netten, S., Visser, A., van Someren, M., Dastani, M., & Dignum, F. (2018). Frame of Reference - Bachelor’s and Master’s Programmes in Artificial Intelligence: The Dutch Perspective. Kunstmatige Intelligentie Opleidingen Nederland (KION).
  • Open Access
    Tanha, J., van Someren, M., & Afsarmanesh, H. (2017). Semi-supervised self-training for decision tree classifiers. International Journal of Machine Learning and Cybernetics, 8(1), 355-370. https://doi.org/10.1007/s13042-015-0328-7
  • Open Access
    Visser, A., De Jong, R., Beks, W., Schlobach, S., van Rooij, R., Homburg, A.-J., van Someren, M., van Maanen, L., & Sluijter, B. (2017). Naar een nieuw curriculum voor de bachelor Kunstmatige Intelligentie. (1.9.1 ed.) UvA. https://staff.fnwi.uva.nl/a.visser/activities/CurriculumCommissieRapport9maart.pdf
  • De Nadai, M., & van Someren, M. (2015). Short-term anomaly detection in gas consumption through ARIMA and Artificial Neural Network forecast. In 2015 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems: July 9th-10th, 2015, Department of Sociology, Room Kessler, via Verdi 26, Trento, Italy : proceedings (pp. 250-255). IEEE. https://doi.org/10.1109/EESMS.2015.7175886
  • Open Access
    Shiarlis, K., Messias, J., van Someren, M., Whiteson, S., Kim, J., Vroon, J., Englebienne, G., Truong, K., Evers, V., Pérez-Higueras, N., Pérez-Hurtado, I., Ramon-Vigo, R., Caballero, F., Merino, L., Shen, J., Petridis, S., Pantic, M., Hedman, L., Scherlund, M., ... Michel, H. (2015). TERESA: A Socially Intelligent Semi-autonomous Telepresence System. Paper presented at Workshop on Machine Learning for Social Robotics at ICRA-15.
  • Open Access
    Netten, C. P. M. (2015). Machine learning for relevance of information in crisis response. [Thesis, fully internal, Universiteit van Amsterdam].
  • Tanha, J., van Someren, M., & Afsarmanesh, H. (2014). Boosting for Multiclass Semi-Supervised Learning. Pattern Recognition Letters, 37, 63-77. https://doi.org/10.1016/j.patrec.2013.10.008
  • de Vries, G. K. D., & van Someren, M. (2014). An analysis of alignment and integral based kernels for machine learning from vessel trajectories. Expert Systems With Applications, 41(16), 7596-7607. https://doi.org/10.1016/j.eswa.2014.05.025
  • Zuccala, A., van Someren, M., & van Bellen, M. (2014). A machine-learning approach to coding book reviews as quality indicators: Toward a theory of megacitation. Journal of the Association for Information Science and Technology, 65(11), 2248-2260. https://doi.org/10.1002/asi.23104
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