1. Learning Complex Uncertain States Changes via Asymmetric Hidden Markov Models: An Industrial Case

    Bueno, M. L. P., Hommersom, A., Lucas, P. J. F., Verwer, S. & Linard, A., 2016, Proceedings of the Eighth International Conference on Probabilistic Graphical Models : The JMLR Workshop and Conference PGM 2016. Antonucci, A., Corani, G. & de Campos, C. P. (eds.). JMLR, Vol. 52. p. 50-61 12 p. (JMLR: Workshop and Conference Proceedings).

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

  2. Learning Decision Trees with Flexible Constraints and Objectives Using Integer Optimization

    Verwer, S. & Zhang, Y., 2017, Integration of AI and OR Techniques in Constraint Programming: CPAIOR 2017. Salvagnin, D. & Lombardi, M. (eds.). Cham: Springer, p. 94-103 10 p. (Lecture Notes in Computer Science; vol. 10335).

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

  3. Learning Deterministic Finite Automata from Innite Alphabets

    Pellegrino, N., Hammerschmidt, C., Verwer, S. & Lin, Q., 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. 69-72 5 p. (JMLR: Workshop and Conference Proceedings).

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

  4. Learning Driving Behavior By Timed Syntactic Pattern Recognition

    Verwer, SE., de Weerdt, MM. & Witteveen, C., 2011, Proceedings of the International Joint Conference on Artificial Intelligence. Walsh, T. (ed.). American Association for Artificial Intelligence (AAAI), p. 1529-1534 6 p.

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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

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