69 Publikationen

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  • [69]
    2024 | Zeitschriftenaufsatz | PUB-ID: 2987814
    Morgenroth T, Begeny CT, Kirby TA, 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.
    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: Proceedings of the 15th International Conference on Educational Data Mining (EDM 2022). Cristea AI, Brown C, Mitrovic T, Bosch N (Eds); 17–26.
    PUB | DOI | Download (ext.)
     
  • [66]
    2022 | Zeitschriftenaufsatz | PUB-ID: 2978970 OA
    Paaßen B, Koprinska I, Yacef K (2022)
    Recursive Tree Grammar Autoencoders.
    Machine Learning 111: 3393–3423.
    PUB | PDF | DOI | Download (ext.)
     
  • [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.
    PUB | PDF | DOI | Download (ext.)
     
  • [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: Proceedings of the Second International Workshop on Multimodal Immersive Learning Systems ({MILeS} 2022). Asyraaf Mat Sanusi K, Limbu B, Schneider J, Di Mitri D, Klemke R (Eds); 22–27.
    PUB | Download (ext.)
     
  • [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: Proceedings of the 23rd International Conference on Artificial Intelligence in Education (AIED 2022) Practitioner’s Track. DeFalco JA, Matos DDM da C, Blanc B, Reichow I (Eds); 132–137.
    PUB | DOI | Download (ext.)
     
  • [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: Proceedings of the 15th International Conference on Educational Data Mining (EDM 2022). Cristea AI, Brown C, Mitrovic T, Bosch N (Eds); 555–559.
    PUB | DOI | Download (ext.)
     
  • [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: Proceedings of the 15th International Conference on Educational Data Mining (EDM 2022). Cristea AI, Brown C, Mitrovic T, Bosch N (Eds); 217–227.
    PUB | DOI | Download (ext.)
     
  • [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.
    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.
    PUB | DOI | Download (ext.)
     
  • [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.
    PUB | DOI | Download (ext.) | arXiv
     
  • [57]
    2021 | Konferenzbeitrag | PUB-ID: 2978996
    Bacciu D, Bianchi FM, Paaßen B, Alippi C (2021)
    Deep learning for graphs.
    In: {Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2021)}. Verleysen M (Ed); 89–98.
    PUB | Download (ext.)
     
  • [56]
    2021 | Konferenzbeitrag | PUB-ID: 2978995
    Paaßen B, Kravčík M (2021)
    Teaching psychomotor skills using machine learning for error detection.
    In: Proceedings of the 1st International Workshop on Multimodal Immersive Learning Systems ({MILeS} 2021). Klemke R, Asyraaf Mat Sanusi K (Eds); 8–14.
    PUB | Download (ext.)
     
  • [55]
    2021 | Konferenzbeitrag | PUB-ID: 2978967
    Paaßen B (2021)
    An A*-algorithm for the Unordered Tree Edit Distance with Custom Costs.
    In: Proceedings of the 14th International Conference on Similarity Search and Applications (SISAP 2021). Reyes N, Connor R, Kriege N, Kazempour D, Bartolini I, Schubert E, Chen J-J (Eds); Springer: 364–371.
    PUB | DOI | Download (ext.) | arXiv
     
  • [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: Proceedings of the 15th {International Conference on Educational Data Mining} ({EDM} 2021). Bouchet F, Vie J-J, Hsiao S, Sahebi S (Eds); International Educational Datamining Society.
    PUB | Download (ext.)
     
  • [53]
    2021 | Konferenzbeitrag | PUB-ID: 2978965
    Paaßen B, Bertsch A, Langer-Fischer K, Rüdian S, Wang X, Sinha R, Kuzilek J, Britsch S, Pinkwart N (2021)
    Analyzing Student Success and Mistakes in Virtual Microscope Structure Search Tasks.
    In: Proceedings of the 15th {International Conference on Educational Data Mining} ({EDM} 2021). Bouchet F, Vie J-J, Hsiao S, Sahebi S (Eds); International Educational Datamining Society.
    PUB | Download (ext.)
     
  • [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: Proceedings of the Twenty-Third Australasian Computing Education Conference (ACE '21). Szabo C, Sheard J (Eds); Association for Computing Machinery: 158–167.
    PUB | DOI
     
  • [51]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2954542
    Paaßen B, Schulz A, Hammer B (2021)
    Reservoir Stack Machines.
    Neurocomputing 470: 352-364.
    PUB | DOI | Download (ext.) | WoS | arXiv
     
  • [50]
    2020 | Konferenzbeitrag | PUB-ID: 2978963
    Paaßen B, Koprinska I, Yacef K (2020)
    Tree Echo State Autoencoders with Grammars.
    In: Proceedings of the 2020 International Joint Conference on Neural Networks ({IJCNN} 2020). Roy A (Ed); 1–8.
    PUB | DOI | Download (ext.) | arXiv
     
  • [49]
    2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2941931
    Paaßen B, Schulz A (2020)
    Reservoir memory machines.
    In: Proceedings of the 28th European Symposium on Artificial Neural Networks (ESANN 2020). Verleysen M (Ed); Bruges: i6doc: 567-572.
    PUB | Download (ext.) | arXiv
     
  • [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.
    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, Aszmann O (2019)
    Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning.
    IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(5): 956-962.
    PUB | PDF | DOI | WoS | PubMed | Europe PMC
     
  • [45]
    2019 | Datenpublikation | PUB-ID: 2941052 OA
    Paaßen B (2019)
    Python Programming Dataset.
    Bielefeld University.
    PUB | Dateien verfügbar | DOI
     
  • [44]
    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2937053
    Paaßen B (2019)
    Adversarial Edit Attacks for Tree Data.
    In: Proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2019). Yin H, Camacho D, Tino P (Eds); Lecture Notes in Computer Science, 11871. Cham: Springer: 359-366.
    PUB | DOI | Download (ext.) | arXiv
     
  • [43]
    2019 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2935953
    Price TW, 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.
    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: Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019). Verleysen M (Ed); Louvain-la-Neuve: i6doc: 477-482.
    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: Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019). Verleysen M (Ed); 85-94.
    PUB | Download (ext.) | arXiv
     
  • [40]
    2019 | Bielefelder E-Dissertation | PUB-ID: 2935545 OA
    Paaßen B (2019)
    Metric Learning for Structured Data.
    Bielefeld: Universität Bielefeld.
    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.
    PUB | DOI | Download (ext.) | WoS | arXiv
     
  • [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.
    PUB | DOI | Download (ext.) | WoS | arXiv
     
  • [37]
    2018 | Datenpublikation | PUB-ID: 2916863 OA
    Paaßen B, Ahmaro A (2018)
    VBB Shortest Path Data 2018.
    Bielefeld University.
    PUB | Dateien verfügbar | DOI
     
  • [36]
    2018 | Datenpublikation | PUB-ID: 2919994 OA
    Paaßen B (2018)
    Tree Edit Distance Learning via Adaptive Symbol Embeddings.
    Bielefeld University.
    PUB | Dateien verfügbar | DOI
     
  • [35]
    2018 | Datenpublikation | PUB-ID: 2916990 OA
    Paaßen B (2018)
    Median Generalized Learning Vector Quantization for Distance Data.
    Bielefeld University.
    PUB | Dateien verfügbar | DOI
     
  • [34]
    2018 | Datenpublikation | PUB-ID: 2916980 OA
    Paaßen B (2018)
    Relational Neural Gas.
    Bielefeld University.
    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: Proceedings of the 35th International Conference on Machine Learning (ICML 2018). Dy J, Krause A (Eds); Proceedings of Machine Learning Research, 80. 3973-3982.
    PUB | Download (ext.) | arXiv
     
  • [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.
    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.
    PUB | Download (ext.) | arXiv
     
  • [29]
    2017 | Datenpublikation | PUB-ID: 2913104 OA
    Paaßen B (2017)
    Time Series Prediction for Relational and Kernel Data.
    Bielefeld University.
    PUB | Dateien verfügbar | DOI
     
  • [28]
    2017 | Datenpublikation | PUB-ID: 2912671 OA
    Paaßen B, Schulz A (2017)
    Linear Supervised Transfer Learning Toolbox.
    Bielefeld University.
    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: Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017). Verleysen M (Ed); Bruges: i6doc.com: 129-134.
    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: Proceedings of the Ninth Mittweida Workshop on Computational Intelligence (MiWoCI 2017). Schleif F-M, Villmann T (Eds); Machine Learning Reports, 32-33.
    PUB | PDF | Download (ext.)
     
  • [25]
    2017 | Datenpublikation | PUB-ID: 2913083 OA
    Paaßen B (2017)
    BinaryAdder UML Dataset.
    Bielefeld University.
    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.
    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: Proceedings of the 16th Conference on Artificial Intelligence in Medicine (AIME 2017). ten Telje A, Popow C, Holmes JH, Sacchi L (Eds); Lecture Notes in Computer Science, 10259. Springer: 338--342.
    PUB | Dateien verfügbar | DOI
     
  • [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: Artificial Neural Networks and Machine Learning - ICANN 2016 - 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. E.P. Villa A, Masulli P, Pons Rivero AJ (Eds); Lecture Notes in Computer Science, 9887. Cham: Springer Nature: 251--258.
    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.
    PUB | PDF | DOI | WoS
     
  • [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: Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Louvain-la-Neuve: Ciaco - i6doc.com: 41--46.
    PUB | PDF
     
  • [19]
    2016 | Datenpublikation | PUB-ID: 2900684 OA
    Paaßen B (2016)
    Java Sorting Programs.
    Bielefeld University.
    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: Proceedings of the 9th International Conference on Educational Data Mining. Barnes T, Chi M, Feng M (Eds); Raleigh, North Carolina, USA: International Educational Datamining Society: 183-190.
    PUB | Download (ext.)
     
  • [17]
    2016 | Datenpublikation | PUB-ID: 2900666 OA
    Paaßen B (2016)
    MiniPalindrome.
    Bielefeld University.
    PUB | Dateien verfügbar | DOI
     
  • [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: Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. E.P. Villa A, Masulli P, Pons Rivero AJ (Eds); Lecture Notes in Computer Science, 9887. Cham: Springer Nature: 510-517.
    PUB | PDF | DOI
     
  • [15]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905855
    Paaßen B, Schulz A, Hammer B (2016)
    Linear Supervised Transfer Learning for Generalized Matrix LVQ.
    In: Proceedings of the Workshop New Challenges in Neural Computation 2016. Hammer B, Martinetz T, Villmann T (Eds); Machine Learning Reports, 11-18.
    PUB | Download (ext.)
     
  • [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: Converging Clinical and Engineering Research on Neurorehabilitation II: Proceedings of the 3rd International Conference on NeuroRehabilitation (ICNR2016). Ibáñez J, Gonzáles-Vargas J, Azorín JM, Akay M, Pons JL (Eds); Springer: 153--157.
    PUB | PDF | DOI | Download (ext.)
     
  • [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.
    PUB | PDF | DOI | Download (ext.) | WoS
     
  • [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: Proceedings of the ESANN, 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); 307-312.
    PUB | PDF
     
  • [11]
    2015 | Report | PUB-ID: 2712107 OA
    Stöckel A, Paaßen B, Dickfelder R, Göpfert JP, Brazda N, Müller HW, Cimiano P, Hartung M, Klinger R (2015)
    SCIE: Information Extraction for Spinal Cord Injury Preclinical Experiments – A Webservice and Open Source Toolkit.
    bioRxive.org.
    PUB | PDF | DOI | Download (ext.)
     
  • [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: Proceedings of the 8th International Conference on Educational Data Mining. Santos OC, Boticario JG, Romero C, Pechenizkiy M, Merceron A, Mitros P, Luna JM, Mihaescu C, Moreno P, Hershkovitz A, Ventura S, Desmarais M (Eds); International Educational Datamining Society: 632-632.
    PUB | Download (ext.)
     
  • [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.
    PUB | PDF | DOI | Download (ext.) | WoS
     
  • [8]
    2015 | Bielefelder Masterarbeit | PUB-ID: 2736686 OA
    Paaßen B (2015)
    Adaptive Affine Sequence Alignment Using Algebraic Dynamic Programming.
    Bielefeld: Bielefeld University.
    PUB | PDF
     
  • [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.
    PUB | DOI | Download (ext.) | WoS
     
  • [6]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673554 OA
    Mokbel B, Paaßen B, Hammer B (2014)
    Adaptive distance measures for sequential data.
    In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 265-270.
    PUB | PDF
     
  • [5]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2683760 OA
    Paaßen B, Stöckel A, Dickfelder R, Göpfert JP, Brazda N, Kirchhoffer T, Müller HW, Klinger R, Hartung M, Cimiano P (2014)
    Ontology-based Extraction of Structured Information from Publications on Preclinical Experiments for Spinal Cord Injury Treatments.
    In: Third Workshop on Semantic Web and Information Extraction (SWAIE). The 25th International Conference on Computational Linguistics (COLING). Maynard D, Erp van M, Davis B (Eds); Dublin, Ireland: 25-32.
    PUB | PDF | Download (ext.)
     
  • [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.
    PUB | DOI | Download (ext.)
     
  • [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: Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings. Wermter S, Weber C, Duch W, Honkela T, Koprinkova-Hristova P, Magg S, Palm G, Villa A (Eds); Lecture Notes in Computer Science, 8681. Springer: 571-578.
    PUB | PDF | DOI | Download (ext.)
     
  • [2]
    2013 | Datenpublikation | PUB-ID: 2692491 OA
    Paaßen B (2013)
    VBB Midi Dataset.
    Bielefeld University.
    PUB | Dateien verfügbar | DOI
     
  • [1]
    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: Proceedings of the 6th International Conference on Educational Data Mining (EDM). D'Mello SK, Calvo RA, Olney A (Eds); 334-335.
    PUB | Download (ext.)
     

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