69 Publikationen

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  • [69]
    2024 | Zeitschriftenaufsatz | PUB-ID: 2987814
    Morgenroth, T., Begeny, C. T., Kirby, T. A., Paaßen, B., & Zeng, Y. (2024). Dissecting Whiteness: consistencies and differences in the stereotypes of lower- and upper-class White US Americans. Self and Identity, 1-25. https://doi.org/10.1080/15298868.2024.2322179
    PUB | DOI | WoS
     
  • [68]
    2023 | Preprint | Veröffentlicht | PUB-ID: 2980970
    Strotherm, J., Müller, A., Hammer, B., & Paaßen, B. (2023). Fairness in KI-Systemen
    PUB | Download (ext.) | arXiv
     
  • [67]
    2022 | Konferenzbeitrag | PUB-ID: 2979001
    Paaßen, B., Dywel, M., Fleckenstein, M., & Pinkwart, N. (2022). Sparse Factor Autoencoders for Item Response Theory. In A. I. Cristea, C. Brown, T. Mitrovic, & N. Bosch (Eds.), Proceedings of the 15th International Conference on Educational Data Mining (EDM 2022) (p. 17–26). https://doi.org/10.5281/zenodo.6853067
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  • [66]
    2022 | Zeitschriftenaufsatz | PUB-ID: 2978970 OA
    Paaßen, B., Koprinska, I., & Yacef, K. (2022). Recursive Tree Grammar Autoencoders. Machine Learning, 111, 3393–3423. https://doi.org/10.1007/s10994-022-06223-7
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  • [65]
    2022 | Zeitschriftenaufsatz | PUB-ID: 2979004 OA
    Paaßen, B., Dehne, J., Krishnaraja, S., Kovalkov, A., Gal, K., & Pinkwart, N. (2022). A conceptual graph-based model of creativity in learning. Frontiers in Education, 7. https://doi.org/10.3389/feduc.2022.1033682
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  • [64]
    2022 | Konferenzbeitrag | PUB-ID: 2979003
    Paaßen, B., Baumgartner, T., Geisen, M., Riedl, N., & Kravčík, M. (2022). Few-shot Keypose Detection for Learning of Psychomotor Skills. In K. Asyraaf Mat Sanusi, B. Limbu, J. Schneider, D. Di Mitri, & R. Klemke (Eds.), Proceedings of the Second International Workshop on Multimodal Immersive Learning Systems ({MILeS} 2022) (p. 22–27).
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  • [63]
    2022 | Konferenzbeitrag | PUB-ID: 2979002
    Paaßen, B., Dywel, M., Fleckenstein, M., & Pinkwart, N. (2022). Interpretable Knowledge Gain Prediction for Vocational Preparatory E-Learnings. In J. A. DeFalco, D. D. M. da C. Matos, B. Blanc, & I. Reichow (Eds.), Proceedings of the 23rd International Conference on Artificial Intelligence in Education (AIED 2022) Practitioner’s Track (p. 132–137). https://doi.org/10.1007/978-3-031-11647-6_23
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  • [62]
    2022 | Konferenzbeitrag | PUB-ID: 2979000
    Paaßen, B., Göpfert, C., & Pinkwart, N. (2022). Faster Confidence Intervals for Item Response Theory via an Approximate Likelihood. In A. I. Cristea, C. Brown, T. Mitrovic, & N. Bosch (Eds.), Proceedings of the 15th International Conference on Educational Data Mining (EDM 2022) (p. 555–559). https://doi.org/10.5281/zenodo.6852950
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  • [61]
    2022 | Konferenzbeitrag | PUB-ID: 2978999
    Picones, G., Paaßen, B., Koprinska, I., & Yacef, K. (2022). Combining domain modelling and student modelling techniques in a single pipeline to support task-sequencing. In A. I. Cristea, C. Brown, T. Mitrovic, & N. Bosch (Eds.), Proceedings of the 15th International Conference on Educational Data Mining (EDM 2022) (p. 217–227). https://doi.org/10.5281/zenodo.6853131
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  • [60]
    2022 | Zeitschriftenaufsatz | PUB-ID: 2978998
    Paaßen, B., Schulz, A., C. Stewart, T., & Hammer, B. (2022). Reservoir Memory Machines as Neural Computers. IEEE Transactions on Neural Networks and Learning Systems, 33(6), 2575–2585. https://doi.org/10.1109/TNNLS.2021.3094139
    PUB | DOI | Download (ext.) | arXiv
     
  • [59]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2978969
    Paaßen, B., McBroom, J., Jeffries, B., Koprinska, I., & Yacef, K. (2021). Mapping Python Programs to Vectors using Recursive Neural Encodings. Journal of Educational Datamining, 13(3), 1–35. https://doi.org/10.5281/zenodo.5634224
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  • [58]
    2021 | Zeitschriftenaufsatz | PUB-ID: 2978997
    Kovalkov, A., Paaßen, B., Segal, A., Pinkwart, N., & Gal, K. (2021). Automatic Creativity Measurement in Scratch Programs Across Modalities. IEEE Transactions on Learning Technologies, 14(6), 740–753. https://doi.org/10.1109/TLT.2022.3144442
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  • [57]
    2021 | Konferenzbeitrag | PUB-ID: 2978996
    Bacciu, D., Bianchi, F. M., Paaßen, B., & Alippi, C. (2021). Deep learning for graphs. In M. Verleysen (Ed.), {Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2021)} (p. 89–98).
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  • [56]
    2021 | Konferenzbeitrag | PUB-ID: 2978995
    Paaßen, B., & Kravčík, M. (2021). Teaching psychomotor skills using machine learning for error detection. In R. Klemke & K. Asyraaf Mat Sanusi (Eds.), Proceedings of the 1st International Workshop on Multimodal Immersive Learning Systems ({MILeS} 2021) (p. 8–14).
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  • [55]
    2021 | Konferenzbeitrag | PUB-ID: 2978967
    Paaßen, B. (2021). An A*-algorithm for the Unordered Tree Edit Distance with Custom Costs. In N. Reyes, R. Connor, N. Kriege, D. Kazempour, I. Bartolini, E. Schubert, & J. - J. Chen (Eds.), Proceedings of the 14th International Conference on Similarity Search and Applications (SISAP 2021) (p. 364–371). Springer. https://doi.org/10.1007/978-3-030-89657-7_27
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  • [54]
    2021 | Konferenzbeitrag | PUB-ID: 2978966
    Kovalkov, A., Paaßen, B., Segal, A., Gal, K., & Pinkwart, N. (2021). Modeling Creativity in Visual Programming: From Theory to Practice. In F. Bouchet, J. - J. Vie, S. Hsiao, & S. Sahebi (Eds.), Proceedings of the 15th {International Conference on Educational Data Mining} ({EDM} 2021) International Educational Datamining Society.
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  • [53]
    2021 | Konferenzbeitrag | PUB-ID: 2978965
    Paaßen, B., Bertsch, A., Langer-Fischer, K., Rüdian, S., Wang, X., Sinha, R., Kuzilek, J., et al. (2021). Analyzing Student Success and Mistakes in Virtual Microscope Structure Search Tasks. In F. Bouchet, J. - J. Vie, S. Hsiao, & S. Sahebi (Eds.), Proceedings of the 15th {International Conference on Educational Data Mining} ({EDM} 2021) International Educational Datamining Society.
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  • [52]
    2021 | Konferenzbeitrag | PUB-ID: 2978964
    McBroom, J., Paaßen, B., Jeffries, B., Koprinska, I., & Yacef, K. (2021). Progress Networks as a Tool for Analysing Student Programming Difficulties. In C. Szabo & J. Sheard (Eds.), Proceedings of the Twenty-Third Australasian Computing Education Conference (ACE '21) (p. 158–167). Association for Computing Machinery. https://doi.org/10.1145/3441636.3442366
    PUB | DOI
     
  • [51]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2954542
    Paaßen, B., Schulz, A., & Hammer, B. (2021). Reservoir Stack Machines. Neurocomputing, 470, 352-364. https://doi.org/10.1016/j.neucom.2021.05.106
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  • [50]
    2020 | Konferenzbeitrag | PUB-ID: 2978963
    Paaßen, B., Koprinska, I., & Yacef, K. (2020). Tree Echo State Autoencoders with Grammars. In A. Roy (Ed.), Proceedings of the 2020 International Joint Conference on Neural Networks ({IJCNN} 2020) (p. 1–8). https://doi.org/10.1109/IJCNN48605.2020.9207165
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  • [49]
    2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2941931
    Paaßen, B., & Schulz, A. (2020). Reservoir memory machines. In M. Verleysen (Ed.), Proceedings of the 28th European Symposium on Artificial Neural Networks (ESANN 2020) (pp. 567-572). Bruges: i6doc.
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  • [48]
    2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2944191
    Morgenroth, T., Stratemeyer, M., & Paaßen, B. (2020). The Gendered Nature and Malleability of Gamer Stereotypes. Cyberpsychology, Behavior, and Social Networking, 23(8), 557-561. doi:10.1089/cyber.2019.0577
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [47]
    2019 | Monographie | PUB-ID: 2935200 OA
    Paaßen, B., Artelt, A., & Hammer, B. (2019). Lecture Notes on Applied Optimization. Faculty of Technology, Bielefeld University.
    PUB | Dateien verfügbar
     
  • [46]
    2019 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2934458 OA
    Prahm, C., Schulz, A., Paaßen, B., Schoisswohl, J., Kaniusas, E., Dorffner, G., Hammer, B., et al. (2019). Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(5), 956-962. doi:10.1109/TNSRE.2019.2907200
    PUB | PDF | DOI | WoS | PubMed | Europe PMC
     
  • [45]
    2019 | Datenpublikation | PUB-ID: 2941052 OA
    Paaßen, B. (2019). Python Programming Dataset. Bielefeld University. doi:10.4119/unibi/2941052
    PUB | Dateien verfügbar | DOI
     
  • [44]
    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2937053
    Paaßen, B. (2019). Adversarial Edit Attacks for Tree Data. In H. Yin, D. Camacho, & P. Tino (Eds.), Lecture Notes in Computer Science: Vol. 11871. Proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2019) (pp. 359-366). Cham: Springer. doi:10.1007/978-3-030-33607-3_39
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  • [43]
    2019 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2935953
    Price, T. W., Dong, Y., Zhi, R., Paaßen, B., Lytle, N., Cateté, V., & Barnes, T. (2019). A Comparison of the Quality of Data-Driven Programming Hint Generation Algorithms. International Journal of Artificial Intelligence in Education, 29(3), 368-395. doi:10.1007/s40593-019-00177-z
    PUB | DOI | WoS
     
  • [42]
    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2933502
    Paaßen, B., Bunge, A., Hainke, C., Sindelar, L., & Vogelsang, M. (2019). Dynamic fairness - Breaking vicious cycles in automatic decision making. In M. Verleysen (Ed.), Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019) (pp. 477-482). Louvain-la-Neuve: i6doc.
    PUB | Download (ext.) | arXiv
     
  • [41]
    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2934571
    Paaßen, B., Gallicchio, C., Micheli, A., & Sperduti, A. (2019). Embeddings and Representation Learning for Structured Data. In M. Verleysen (Ed.), Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019) (pp. 85-94).
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  • [40]
    2019 | Bielefelder E-Dissertation | PUB-ID: 2935545 OA
    Paaßen, B. (2019). Metric Learning for Structured Data. Bielefeld: Universität Bielefeld. doi:10.4119/unibi/2935545
    PUB | PDF | DOI
     
  • [39]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2911900
    Paaßen, B., Göpfert, C., & Hammer, B. (2018). Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces. Neural Processing Letters, 48(2), 669-689. doi:10.1007/s11063-017-9684-5
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  • [38]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2914505
    Paaßen, B., Schulz, A., Hahne, J., & Hammer, B. (2018). Expectation maximization transfer learning and its application for bionic hand prostheses. Neurocomputing, 298, 122-133. doi:10.1016/j.neucom.2017.11.072
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  • [37]
    2018 | Datenpublikation | PUB-ID: 2916863 OA
    Paaßen, B., & Ahmaro, A. (2018). VBB Shortest Path Data 2018. Bielefeld University. doi:10.4119/unibi/2916863
    PUB | Dateien verfügbar | DOI
     
  • [36]
    2018 | Datenpublikation | PUB-ID: 2919994 OA
    Paaßen, B. (2018). Tree Edit Distance Learning via Adaptive Symbol Embeddings. Bielefeld University. doi:10.4119/unibi/2919994
    PUB | Dateien verfügbar | DOI
     
  • [35]
    2018 | Datenpublikation | PUB-ID: 2916990 OA
    Paaßen, B. (2018). Median Generalized Learning Vector Quantization for Distance Data. Bielefeld University. doi:10.4119/unibi/2916990
    PUB | Dateien verfügbar | DOI
     
  • [34]
    2018 | Datenpublikation | PUB-ID: 2916980 OA
    Paaßen, B. (2018). Relational Neural Gas. Bielefeld University. doi:10.4119/unibi/2916980
    PUB | Dateien verfügbar | DOI
     
  • [33]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2913389
    Paaßen, B., Hammer, B., Price, T., Barnes, T., Gross, S., & Pinkwart, N. (2018). The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces. Journal of Educational Data Mining, 10(1), 1-35.
    PUB | Download (ext.) | arXiv
     
  • [32]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2919844
    Paaßen, B., Gallicchio, C., Micheli, A., & Hammer, B. (2018). Tree Edit Distance Learning via Adaptive Symbol Embeddings. In J. Dy & A. Krause (Eds.), Proceedings of Machine Learning Research: Vol. 80. Proceedings of the 35th International Conference on Machine Learning (ICML 2018) (pp. 3973-3982).
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  • [31]
    2018 | Konferenzbeitrag | PUB-ID: 2916318
    Berger, K., Schulz, A., Paaßen, B., & Hammer, B. (2018). Linear Supervised Transfer Learning for the Large Margin Nearest Neighbor Classifier. Presented at the SSCI CIDM 2017. doi:10.1109/SSCI.2017.8285359
    PUB | DOI
     
  • [30]
    2018 | Preprint | Entwurf | PUB-ID: 2919918
    Paaßen, B. (Draft). Revisiting the tree edit distance and its backtracing: A tutorial. arXiv:1805.06869
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  • [29]
    2017 | Datenpublikation | PUB-ID: 2913104 OA
    Paaßen, B. (2017). Time Series Prediction for Relational and Kernel Data. Bielefeld University. doi:10.4119/unibi/2913104
    PUB | Dateien verfügbar | DOI
     
  • [28]
    2017 | Datenpublikation | PUB-ID: 2912671 OA
    Paaßen, B., & Schulz, A. (2017). Linear Supervised Transfer Learning Toolbox. Bielefeld University. doi:10.4119/unibi/2912671
    PUB | Dateien verfügbar | DOI
     
  • [27]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909369 OA
    Paaßen, B., Schulz, A., Hahne, J., & Hammer, B. (2017). An EM transfer learning algorithm with applications in bionic hand prostheses. In M. Verleysen (Ed.), Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017) (pp. 129-134). Bruges: i6doc.com.
    PUB | PDF
     
  • [26]
    2017 | Kurzbeitrag Konferenz / Poster | Veröffentlicht | PUB-ID: 2914663 OA
    Paaßen, B. (2017). Two or three things we do (not) know about distances. In F. - M. Schleif & T. Villmann (Eds.), Machine Learning Reports. Proceedings of the Ninth Mittweida Workshop on Computational Intelligence (MiWoCI 2017) (pp. 32-33).
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  • [25]
    2017 | Datenpublikation | PUB-ID: 2913083 OA
    Paaßen, B. (2017). BinaryAdder UML Dataset. Bielefeld University. doi:10.4119/unibi/2913083
    PUB | Dateien verfügbar | DOI
     
  • [24]
    2017 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2905302 OA
    Paaßen, B., Morgenroth, T., & Stratemeyer, M. (2017). What is a True Gamer? The Male Gamer Stereotype and the Marginalization of Women in Video Game Culture. Sex Roles, 76(7-8), 421-435. doi:10.1007/s11199-016-0678-y
    PUB | PDF | DOI | WoS
     
  • [23]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909037 OA
    Prahm, C., Schulz, A., Paaßen, B., Aszmann, O., Hammer, B., & Dorffner, G. (2017). Echo State Networks as Novel Approach for Low-Cost Myoelectric Control. In A. ten Telje, C. Popow, J. H. Holmes, & L. Sacchi (Eds.), Lecture Notes in Computer Science: Vol. 10259. Proceedings of the 16th Conference on Artificial Intelligence in Medicine (AIME 2017) (pp. 338--342). Springer. doi:10.1007/978-3-319-59758-4_40
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  • [22]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909367
    Kummert, J., Paaßen, B., Jensen, J., Göpfert, C., & Hammer, B. (2016). Local Reject Option for Deterministic Multi-class SVM. In A. E.P. Villa, P. Masulli, & A. J. Pons Rivero (Eds.), Lecture Notes in Computer Science: Vol. 9887. Artificial Neural Networks and Machine Learning - ICANN 2016 - 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II (pp. 251--258). Cham: Springer Nature. doi:10.1007/978-3-319-44781-0_30
    PUB | DOI
     
  • [21]
    2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2783224 OA
    Paaßen, B., Mokbel, B., & Hammer, B. (2016). Adaptive structure metrics for automated feedback provision in intelligent tutoring systems. Neurocomputing, 192(SI), 3-13. doi:10.1016/j.neucom.2015.12.108
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  • [20]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900676 OA
    Paaßen, B., Göpfert, C., & Hammer, B. (2016). Gaussian process prediction for time series of structured data. In M. Verleysen (Ed.), Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 41--46). Louvain-la-Neuve: Ciaco - i6doc.com.
    PUB | PDF
     
  • [19]
    2016 | Datenpublikation | PUB-ID: 2900684 OA
    Paaßen, B. (2016). Java Sorting Programs. Bielefeld University. doi:10.4119/unibi/2900684
    PUB | Dateien verfügbar | DOI
     
  • [18]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904509
    Paaßen, B., Jensen, J., & Hammer, B. (2016). Execution Traces as a Powerful Data Representation for Intelligent Tutoring Systems for Programming. In T. Barnes, M. Chi, & M. Feng (Eds.), Proceedings of the 9th International Conference on Educational Data Mining (pp. 183-190). Raleigh, North Carolina, USA: International Educational Datamining Society.
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  • [17]
    2016 | Datenpublikation | PUB-ID: 2900666 OA
    Paaßen, B. (2016). MiniPalindrome. Bielefeld University. doi:10.4119/unibi/2900666
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  • [16]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905729 OA
    Göpfert, C., Paaßen, B., & Hammer, B. (2016). Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning. In A. E.P. Villa, P. Masulli, & A. J. Pons Rivero (Eds.), Lecture Notes in Computer Science: Vol. 9887. Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II (pp. 510-517). Cham: Springer Nature. doi:10.1007/978-3-319-44778-0_60
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  • [15]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905855
    Paaßen, B., Schulz, A., & Hammer, B. (2016). Linear Supervised Transfer Learning for Generalized Matrix LVQ. In B. Hammer, T. Martinetz, & T. Villmann (Eds.), Machine Learning Reports. Proceedings of the Workshop New Challenges in Neural Computation 2016 (pp. 11-18).
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  • [14]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904178 OA
    Prahm, C., Paaßen, B., Schulz, A., Hammer, B., & Aszmann, O. (2016). Transfer Learning for Rapid Re-calibration of a Myoelectric Prosthesis after Electrode Shift. In J. Ibáñez, J. Gonzáles-Vargas, J. M. Azorín, M. Akay, & J. L. Pons (Eds.), Converging Clinical and Engineering Research on Neurorehabilitation II: Proceedings of the 3rd International Conference on NeuroRehabilitation (ICNR2016) (pp. 153--157). Springer. doi:10.1007/978-3-319-46669-9_28
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  • [13]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2710031 OA
    Mokbel, B., Paaßen, B., Schleif, F. - M., & Hammer, B. (2015). Metric learning for sequences in relational LVQ. Neurocomputing, 169(SI), 306-322. doi:10.1016/j.neucom.2014.11.082
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  • [12]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2724156 OA
    Paaßen, B., Mokbel, B., & Hammer, B. (2015). Adaptive structure metrics for automated feedback provision in Java programming. In M. Verleysen (Ed.), Proceedings of the ESANN, 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 307-312).
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  • [11]
    2015 | Report | PUB-ID: 2712107 OA
    Stöckel, A., Paaßen, B., Dickfelder, R., Göpfert, J. P., Brazda, N., Müller, H. W., Cimiano, P., et al. (2015). SCIE: Information Extraction for Spinal Cord Injury Preclinical Experiments – A Webservice and Open Source Toolkit. bioRxive.org. doi:10.1101/013458
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  • [10]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2762087
    Paaßen, B., Mokbel, B., & Hammer, B. (2015). A Toolbox for Adaptive Sequence Dissimilarity Measures for Intelligent Tutoring Systems. In O. C. Santos, J. G. Boticario, C. Romero, M. Pechenizkiy, A. Merceron, P. Mitros, J. M. Luna, et al. (Eds.), Proceedings of the 8th International Conference on Educational Data Mining (pp. 632-632). International Educational Datamining Society.
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  • [9]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2752955 OA
    Walter, O., Häb-Umbach, R., Mokbel, B., Paaßen, B., & Hammer, B. (2015). Autonomous Learning of Representations. KI - Künstliche Intelligenz, 29(4), 339–351. doi:10.1007/s13218-015-0372-1
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  • [8]
    2015 | Bielefelder Masterarbeit | PUB-ID: 2736686 OA
    Paaßen, B. (2015). Adaptive Affine Sequence Alignment Using Algebraic Dynamic Programming. Bielefeld: Bielefeld University.
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  • [7]
    2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2678214
    Hofmann, D., Schleif, F. - M., Paaßen, B., & Hammer, B. (2014). Learning interpretable kernelized prototype-based models. Neurocomputing, 141, 84-96. doi:10.1016/j.neucom.2014.03.003
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  • [6]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673554 OA
    Mokbel, B., Paaßen, B., & Hammer, B. (2014). Adaptive distance measures for sequential data. In M. Verleysen (Ed.), ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 265-270). Bruges, Belgium: i6doc.com.
    PUB | PDF
     
  • [5]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2683760 OA
    Paaßen, B., Stöckel, A., Dickfelder, R., Göpfert, J. P., Brazda, N., Kirchhoffer, T., Müller, H. W., et al. (2014). Ontology-based Extraction of Structured Information from Publications on Preclinical Experiments for Spinal Cord Injury Treatments. In D. Maynard, M. Erp van, & B. Davis (Eds.), Third Workshop on Semantic Web and Information Extraction (SWAIE). The 25th International Conference on Computational Linguistics (COLING) (pp. 25-32). Dublin, Ireland.
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  • [4]
    2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2734058
    Gross, S., Mokbel, B., Paaßen, B., Hammer, B., & Pinkwart, N. (2014). Example-based feedback provision using structured solution spaces. International Journal of Learning Technology, 9(3), 248-280. doi:10.1504/IJLT.2014.065752
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  • [3]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2710067 OA
    Mokbel, B., Paaßen, B., & Hammer, B. (2014). Efficient Adaptation of Structure Metrics in Prototype-Based Classification. In S. Wermter, C. Weber, W. Duch, T. Honkela, P. Koprinkova-Hristova, S. Magg, G. Palm, et al. (Eds.), Lecture Notes in Computer Science: Vol. 8681. Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings (pp. 571-578). Springer. doi:10.1007/978-3-319-11179-7_72
    PUB | PDF | DOI | Download (ext.)
     
  • [2]
    2013 | Datenpublikation | PUB-ID: 2692491 OA
    Paaßen, B. (2013). VBB Midi Dataset. Bielefeld University. doi:10.4119/unibi/2692491
    PUB | Dateien verfügbar | DOI
     
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    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625185
    Mokbel, B., Gross, S., Paaßen, B., Pinkwart, N., & Hammer, B. (2013). Domain-Independent Proximity Measures in Intelligent Tutoring Systems. In S. K. D'Mello, R. A. Calvo, & A. Olney (Eds.), Proceedings of the 6th International Conference on Educational Data Mining (EDM) (pp. 334-335).
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