1. A data-driven behavior generation algorithm in car-following scenarios

    Zhang, Y., Lin, Q., Wang, J., Verwer, S. & Dolan, J. M., 2018, Dynamics of Vehicles on Roads and Tracks: Proceedings of the 25th International Symposium on Dynamics of Vehicles on Roads and Tracks (IAVSD 2017) . Spiryagin, M., Gordon, T., Cole, C. & McSweeney, T. (eds.). Leiden: CRC Press, Vol. 1. p. 1-2 2 p.

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

  2. A likelihood-ratio test for identifying probabilistic deterministic real-time automata from positive data

    Verwer, SE., de Weerdt, MM. & Witteveen, C., 2010, Grammatical Inference: Theoretical Results and Applications. Sempere, JM. & García, P. (eds.). Berlin: Springer, p. 203-216 14 p. (Lecture Notes in Computer Science; vol. 6339).

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

  3. An Experience Report on Applying Passive Learning in a Large-Scale Payment Company

    Wieman, R., Finavaro Aniche, M., Lobbezoo, W., Verwer, S. & van Deursen, A., 2017, Proceedings - 2017 IEEE International Conference on Software Maintenance and Evolution, ICSME 2017. Los Alamitos, CA: IEEE Computer Society, p. 564-573 10 p.

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

  4. Anomaly Detection in a Digital Video Broadcasting System Using Timed Automata

    Liu, X., Lin, Q., Verwer, S. & Jarnikov, D., 24 May 2017, ArXiv Preprint.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientific

  5. Auction optimization using regression trees and linear models as integer programs

    Zhang, Y., Verwer, S. & Ye, Q. C., 2017, In : Artificial Intelligence. 244, p. 368-395 28 p.

    Research output: Contribution to journalArticleScientificpeer-review

  6. Behavioral Clustering of Non-Stationary IP Flow Record Data

    Hammerschmidt, C., Marchal, S., State, R. & Verwer, S., Nov 2016, 12th International Conference on Network and Service Management CNSM 2016. Piscataway, NJ: IEEE, p. 253-257 5 p.

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

  7. Bigger is not always better: on the quality of hypotheses in active automata learning

    Smetsers, R., Volpato, M., Vaandrager, FW. & Verwer, SE., 2014, Proceedings of the 12th International Conference of Grammatical Inference. Clark, A., Kanazawa, M. & Yoshinaka, R. (eds.). p. 167-181 15 p. (JMLR Workshop and Conference Proceedings; vol. 34).

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

  8. Car-following Behavior Model Learning Using Timed Automata

    Zhang, Y., Lin, Q., Wang, J. & Verwer, S., Jul 2017, IFAC-PapersOnLine. Dochain, D., Henrion, D. & Peaucelle, D. (eds.). Elsevier, p. 2353-2358 6 p. (IFAC-PapersOnLine; vol. 50, no. 1).

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

  9. Efficient Identification of Timed Automata: Theory and Practice

    Verwer, SE., 2010, Delft. 252 p.

    Research output: ThesisDissertation (TU Delft)Scientific

  10. Efficient Learning of Communication Profiles from IP Flow Records

    Hammerschmidt, C., Marchal, S., State, R., Pellegrino, N. & Verwer, S., 2016, Proceedings - 2016 IEEE 41st Conference on Local Computer Networks, LCN 2016. Kellenberger, P. (ed.). Los Alamitos, CA: IEEE, p. 1-4 4 p.

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

  11. Efficiently learning simple timed automata

    Verwer, SE., de Weerdt, MM. & Witteveen, C., 2008, Induction of Process Models (IPM 2008). Bridewell, W., Calders, T., de Medeiros, A. K., Kramer, S., Pechenizkiy, M. & Todorovski, L. (eds.). Antwerp: University of Antwerp, p. 61-68 8 p.

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

  12. Efficiently learning timed models from observations

    Verwer, SE., de Weerdt, MM. & Witteveen, C., 2008, Benelearn 2008. Wehenkel, L., Geurts, P. & Maree, R. (eds.). Luik: University of Liege, p. 75-76 2 p.

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

  13. Exact DFA Identification Using SAT Solvers

    Heule, MJH. & Verwer, SE., 2010, Grammatical Inference: Theoretical Results and Applications 10th International Colloquium, ICGI 2010. Sempere, JM. & García, P. (eds.). Berlin: Springer, p. 66-79 14 p. (Lecture Notes in Computer Science; vol. 6339).

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

  14. Flexible State-Merging for learning (P)DFAs in Python

    Hammerschmidt, C., Loos, B., State, R., Engel, T. & Verwer, S., 2016, Proceedings of The 13th International Conference on Grammatical Inference: The JMLR Workshop and Conference, The Sequence PredictIction ChallengE (SPiCe). Verwer, S., van Zaanen, M. & Smetsers, R. (eds.). JMLR, Vol. 57. p. 154-159 6 p. (JMLR: Workshop and Conference Proceedings).

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

  15. Human in the Loop: Interactive Passive Automata Learning via Evidence-Driven State-Merging Algorithms

    Hammerschmidt, C. A., State, R. & Verwer, S., 2017, ArXiv Preprint. Vol. abs/1707.09430.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientific

  16. Identifying an automaton model for timed data

    Verwer, SE., de Weerdt, MM. & Witteveen, C., 2006, Proceedings of the Annual Machine Learning Conference of Belgium and the Netherlands (Benelearn). Saeys, Y., Tsiporkova, E., Baets, B. D. & van de Peer, Y. (eds.). Benelearn, p. 57-64 8 p.

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

  17. Identifying an automaton model for timed data (extended abstract)

    Verwer, SE., de Weerdt, MM. & Witteveen, C., 2006, Proceedings of the Belgium-Dutch Conference on Artificial Intelligence (BNAIC). Schobbens, P-Y., Vanhoof, W. & Schwanen, G. (eds.). BNVKI, p. 439-440 2 p.

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

  18. Improved privacy of dynamic group services

    Veugen, T., Doumen, J., Erkin, Z., Pellegrino, N., Verwer, S. & Weber, J., 2017, In : Eurasip Journal on Information Security. 2017, 1, p. 1-9 9 p., 3.

    Research output: Contribution to journalArticleScientificpeer-review

  19. Improving active Mealy machine learning for protocol conformance testing

    Aarts, F., Kuppens, H., Tretmans, J., Vaandrager, FW. & Verwer, SE., 2014, In : Machine Learning. 96, 1-2, p. 189-224 36 p.

    Research output: Contribution to journalArticleScientificpeer-review

  20. Lane-Change Intention Estimation for Car-Following Control in Autonomous Driving

    Zhang, Y., Lin, Q., Wang, J., Verwer, S. & Dolan, J. M., 2018, In : IEEE Transactions on Intelligent Vehicles. 3, 3, p. 276-286 11 p.

    Research output: Contribution to journalArticleScientificpeer-review

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