1. 2016
  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. Proceedings of the 13th International Conference on Grammatical Inference ICGI: JMLR Workshop and Conference Proceedings

    Verwer, S. (ed.), van Zaanen, M. (ed.) & Smetsers, R. (ed.), 2016, JMLR. 169 p. (JMLR: Workshop and Conference Proceedings)

    Research output: Book/ReportBook editingScientificpeer-review

  8. 2014
  9. 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

  10. 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

  11. 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

  12. Regular inference as vertex coloring

    Florêncio, CC. & Verwer, SE., 2014, In : Theoretical Computer Science. 558, p. 18-34 17 p.

    Research output: Contribution to journalArticleScientificpeer-review

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