1. 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. Efficient Identification of Timed Automata: Theory and Practice

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

    Research output: ThesisDissertation (TU Delft)Scientific

  7. 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

  8. 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

  9. 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

  10. 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

ID: 172770