Ehemaliger Mitarbeiter
Lehrstuhl für Maschinelles Lernen und Data Engineering (Prof. Gieseke)
Leonardo Campus 3
48149
Münster
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Leonardo Campus 3
48149
Münster
Lima, M., Denis, M. L., Christian;, G., & Fabian, (2023). End-to-End Neural Network Training for Hyperbox-Based Classification. In Proceedings of the 31th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Brügge. (accepted / in press (not yet published))
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Lülf, C., Lima Martins, D. M., Vaz, S. M. A., Zhou, Y., & Gieseke, F. (2023). Fast Search-By-Classification for Large-Scale Databases Using Index-Aware Decision Trees and Random Forests. In VLDB, E. (Ed.), Proceedings of the VLDB Endowment (11, pp. 2845–2857). Vancouver: ACM Press.
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Lülf, C., Lima Martins, D. M., Vaz, S. M. A., Zhou, Y., & Gieseke, F. (2023). RapidEarth: A Search Engine for Large-Scale Geospatial Imagery. In Proceedings of the ACM SIGSPATIAL 2023, Hamburg. (accepted / in press (not yet published))
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Martins, D. M. L., & Vossen, G. (2023). Self-Organizing Maps for Data Purchase Support in Data Marketplaces. In Ngoc, T. N., János, B., László, G., Manuel, N., Jan, T., Gottfried, V., & Adrianna, K. (Eds.), Computational Collective Intelligence (pp. 43–55). Lecture Notes in Computer Science: Vol. 14162. Heidelberg: Springer International Publishing.
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Carvalho, T., Martins, D., Lima, N. F., & Vossen, G. (2022). Recommending View Bundles in Data Marketplaces. In IEEE, (Ed.), IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022, Prague, Czech Republic, October 9-12, 2022 (pp. 3403–3408). Prague, Czech Republic: IEEE.
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Czarnowski, I., & Martins, D. (2022). Impact of Clustering on a Synthetic Instance Generation in Imbalanced Data Streams Classification. In Groen, D., Mulatier, C., Paszynski, M., Krzhizhanovskaya, V., Dongarra, J., & Sloot, P. (Eds.), Computational Science — {ICCS} 2022 — 22nd International Conference, London, UK, June 21-23, 2022, Proceedings, Part {II} (pp. 586–597). Lecture Notes in Computer Science: Vol. 13351. Cham: Springer.
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Souza, A. J., Martins, D., & Lima, N. F. (2022). Evolving Interpretable Classification Models via Readability-Enhanced Genetic Programming. In Ishibuchi, H., Kwoh, C.-K., Tan, A.-H., Srinivasan, D., Miao, C., Trivedi, A., & Crockett, K. (Eds.), Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence (SSCI 2022), 4 — 7 December 2022, Singapore (pp. 1691–1697). Singapur: IEEE.
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Carvalho, T., Martins, D., & Lima, N. F. (2021). MapView: Exploring Datasets via Unsupervised View Recommendation. In unknown, u., & , (Eds.), {IEEE} Latin American Conference on Computational Intelligence, {LA-CCI} 2021, Temuco, Chile, November 2-4, 2021 (pp. 1–6). Temuco, Chile: IEEE.
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Silva, R. E., Martins, D., & Lima, N. F. (2021). Automatic Feature Engineering Using Self-Organizing Maps. In Unknown, U., & , (Eds.), {IEEE} Latin American Conference on Computational Intelligence, {LA-CCI} 2021, Temuco, Chile, November 2-4, 2021 (pp. 1–6). Temuco, Chile: IEEE.
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Silva, R. E., Martins, D., & Lima, N. F. (2021). Self-Organizing Transformations for Automatic Feature Engineering. In unknown, u., & , (Eds.), 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1–7). Orlando: IEEE.
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Maleszka, M., Maleszka, B., Krol, D., Hernes, M., Martins, D., Homann, L., & Vossen, G. (2020). A Modular Diversity Based Reviewer Recommendation System. In Paweł, S., Marcin, P., Marek, K., & Chutimet, S. (Eds.), Intelligent Information and Database Systems (pp. 550–561). Communications in Computer and Information Science (CCIS): Vol. 1178. Heidelberg: Springer.
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Homann, L., Martins, D., Vossen, G., & Kraume, K. (2019). Enhancing Traditional Recommender Systems via Social Communities. Vietnam Journal of Computer Science, 6(1), 3–16.
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Homann, L., Martins, D., Vossen, G., & Kraume, K. (2019). Enhancing Traditional Recommender Systems via Social Communities. Vietnam Journal of Computer Science, 6(1), 3–16.
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Martins, D. (2019). Reverse engineering database queries from examples: State-of-the-art, challenges, and research opportunities. Information Systems, 83, 89–100.
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Martins, D. (2019). Enabling non-technical users to query and purchase data. at the University of Mü}nster, Germany. Münster.
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Lima, M., Denis, M. L., Jens;, V., & Gottfried, (2019). Supporting Customers with Limited Budget in Data Marketplaces.
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Homann, L., Maleszka, B., Martins, D., & Vossen, G. (2018). A Generic Framework for Collaborative Filtering Based on Social Collective Recommendation. In Ngoc, T. N., Elias, P., Zaheer, K., & Bogdan, T. (Eds.), Computational Collective Intelligence (pp. 238–247). Lecture Notes in Computer Science: Vol. 11055. Heidelberg: Springer International Publishing.
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Homann, L., Maleszka, B., Martins, D., & Vossen, G. (2018). A Generic Framework for Collaborative Filtering Based on Social Collective Recommendation. In Proceedings of the 10th International Conference (ICCCI) 2018, Bristol, UK, 238–247.
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Kr{ó}}tkiewicz, M., Wojtkiewicz, K., & Martins, D. (2018). Influence Power Factor for User Interface Recommendation System. In Proceedings of the International Conference on Computational Collective Intelligence, Bristol, UK, 228–237.
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Martins, D., Vossen, G., & Lima, N. F. (2018). Discovering {SQL} Queries from Examples using Intelligent Algorithms. In Proceedings of the 2018 IEEE Latin American Conference on Computational Intelligence (LA-CCI), Gudalajara, Mexico, 1–6.
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Martins, D., Vossen, G., & Maleszka, M. (2018). Supporting Online Data Purchase by Preference Recommendation. In Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2018), Miyazaki, Japan.
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Buarque, d. L. N., Fernando;, M., Denis;, V., & Gottfried, (2018). A Semiotic-Inspired Machine for Personalized Multi-Criteria Intelligent Decision Support. Data & Knowledge Engineering, 117, 225–238.
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Kasper, G., Diego, d. S. B., & Denis, M. L. M. a. B. H. (2017). User Profile Acquisition: A Comprehensive Framework to Support Personal Information Agents. In Proceedings of the Latin America Conference on Computational Intelligence (LA-CCI 2017), Arequipa, Peru.
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Martins, D., Buarque, d. L. N. F., & Vossen, G. (2017). Learning Database Queries via Intelligent Semiotic Machines. In Proceedings of the 4th IEEE Latin American Conference on Computational Intelligence (LA-CCI), Arequipa, Peru.
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Martins, D., Vossen, G., & Buarque, d. L. N. F. (2017). Intelligent Decision Support for Data Purchase. In Proceedings of the 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI), Leipzig, Germany, 396–402.
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Martins, D., & Lima, N. F. (2017). Hybrid Intelligent Decision Support Using a Semiotic Case-Based Reasoning and Self-Organizing Maps. IEEE Transactions on Systems, Man, and Cybernetics: Systems, PP(99), 1–8.
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