1. Polynomial distinguishability of timed automata

    Verwer, SE., de Weerdt, MM. & Witteveen, C., 2008, ICGI. Clark, A., Coste, F. & Miclet, L. (eds.). Springer, p. 238-251 14 p. (Lecture Notes in Artificial Intelligence; vol. 5278).

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

  2. One-Clock Deterministic Timed Automata Are Efficiently Identifiable in the Limit

    Verwer, SE., de Weerdt, MM. & Witteveen, C., 2009, Third International Conference, LATA 2009, Tarragona, Spain, April 2-8, 2009. Proceedings. Dediu, AH., Ionescu, AM. & Martin-Vide, C. (eds.). Berlin: Springer, p. 740-751 12 p. (Lecture Notes in Computer Science; vol. 5457).

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

  3. On the identifiability in the limit of timed automata

    Verwer, SE., de Weerdt, MM. & Witteveen, C., 2006, Proceedings of the Grammatical inference workshop on open problems and new directions. p. -

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

  4. Modeling and analysis of non-homogenous fabrication/assembly systems with multiple failure modes

    Wang, J-Q., Yan, F-Y., Cui, P-H., Xia, T., Cui, F-D. & Verwer, S., 2017, In : International Journal of Advanced Manufacturing Technology. p. 1-17 17 p.

    Research output: Contribution to journalArticleScientificpeer-review

  5. Merging partially labelled trees: hardness and a declarative programming solution

    Labarre, A. & Verwer, SE., 2014, In : IEEE - ACM Transactions on Computational Biology and Bioinformatics. 11, 2, p. 389-397 9 p.

    Research output: Contribution to journalArticleScientificpeer-review

  6. MOHA: A Multi-Mode Hybrid Automaton Model for Learning Car-Following Behaviors

    Lin, Q., Zhang, Y., Verwer, S. & Wang, J., 2019, In : IEEE Transactions on Intelligent Transportation Systems. 20, 2, p. 790-796 7 p.

    Research output: Contribution to journalArticleScientificpeer-review

  7. Learning optimal classification trees using a binary linear program formulation

    Verwer, S. & Zhang, Y., 2019, Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence . Palo ALto: Association for the Advancement of Artificial Intelligence (AAAI), Vol. 33. p. 1625-1632 8 p.

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

  8. Learning fuzzy decision trees using integer programming

    Rhuggenaath, J. S., Zhang, Y., Akcay, A., Kaymak, U. & Verwer, S., 1 Feb 2018, 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). Piscataway: IEEE, p. 1-8 8 p.

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

  9. Learning behavioral fingerprints from Netflows using Timed Automata

    Pellegrino, N., Lin, Q., Hammerschmidt, C. & Verwer, S., 24 Jul 2017, 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM). Chemouil, P., Monteiro, E., Charalambides, M., Madeira, E., Simoes, P., Secci, S., Gaspary, L. P. & dos Santos, C. R. P. (eds.). IEEE, p. 308-316 9 p.

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

  10. Learning Pairwise Disjoint Simple Languages from Positive Examples

    Linard, A., Smetsers, R., Vaandrager, FW., Waqas, U., van Pinxten, J. & Verwer, S., 6 Jun 2017, ArXiv Preprint. 4 p.

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

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