PUB - Publikationen an der Universität Bielefeld

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• [17]
  

  2023 | Bielefelder E-Dissertation | PUB-ID: 2968265

  Göpfert, C., 2023. Guiding Information: Supervised Models and their Relationship with Data, Bielefeld: Universität Bielefeld.

  PUB | PDF | DOI
   
• [16]
  

  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, et al., eds. Proceedings of the 15th International Conference on Educational Data Mining (EDM 2022). pp. 555–559.

  PUB | DOI | Download (ext.)
   
• [15]
  

  2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2957385

  Risse, N., Göpfert, C., & Göpfert, J.P., 2021. How to Compare Adversarial Robustness of Classifiers from a Global Perspective. In I. Farkaš, et al., eds. Artificial Neural Networks and Machine Learning – ICANN 2021. 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part I. Lecture Notes in Computer Science. no.12891 Cham: Springer International Publishing, pp. 29-41.

  PUB | DOI
   
• [14]
  

  2021 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2955115

  Straat, M., et al., 2021. Supervised learning in the presence of concept drift: a modelling framework. Neural Computing and Applications.

  PUB | DOI | WoS
   
• [13]
  

  2020 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982081

  Biehl, M., et al., 2020. Prototype-Based Classifiers in the Presence of Concept Drift: A Modelling Framework. In A. Vellido, et al., eds. Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. Proceedings of the 13th International Workshop, WSOM+ 2019, Barcelona, Spain, June 26-28, 2019. Advances in Intelligent Systems and Computing. Cham: Springer International Publishing, pp. 210-221.

  PUB | DOI
   
• [12]
  

  2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2935925

  Göpfert, C., et al., 2019. When can unlabeled data improve the learning rate? In Conference on Learning Theory (COLT).

  PUB | PDF | arXiv
   
• [11]
  

  2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2935456

  Pfannschmidt, L., et al., 2019. FRI - Feature Relevance Intervals for Interpretable and Interactive Data Exploration. Presented at the 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, Certosa di Pontignano, Siena - Tuscany, Italy.

  PUB | PDF | DOI | arXiv
   
• [10]
  

  2019 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2933715

  Brinkrolf, J., Göpfert, C., & Hammer, B., 2019. Differential privacy for learning vector quantization. Neurocomputing, 342, p 125-136.

  PUB | PDF | DOI | WoS
   
• [9]
  

  2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2932412

  Straat, M., et al., 2018. Statistical Mechanics of On-Line Learning Under Concept Drift. ENTROPY, 20(10): 775.

  PUB | DOI | WoS | PubMed | Europe PMC
   
• [8]
  

  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), p 669-689.

  PUB | DOI | Download (ext.) | WoS | arXiv
   
• [7]
  

  2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2915273

  Göpfert, C., et al., 2018. Interpretation of Linear Classifiers by Means of Feature Relevance Bounds. Neurocomputing, 298, p 69-79.

  PUB | PDF | DOI | WoS
   
• [6]
  

  2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2908201

  Göpfert, C., Pfannschmidt, L., & Hammer, B., 2017. Feature Relevance Bounds for Linear Classification. In M. Verleysen, ed. Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve: Ciaco - i6doc.com, pp. 187--192.

  PUB | Dateien verfügbar | Download (ext.)
   
• [5]
  

  2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2913752

  Göpfert, J.P., et al., 2017. Effects of Variability in Synthetic Training Data on Convolutional Neural Networks for 3D Head Reconstruction. In 2017 SSCI Proceedings. 2017 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, NJ: IEEE.

  PUB | PDF | DOI
   
• [4]
  

  2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2915274

  Göpfert, C., Göpfert, J.P., & Hammer, B., 2017. Analyzing Feature Relevance for Linear Reject Option SVM using Relevance Intervals. In Proceedings of the 2017 NIPS workshop on Transparent and Interpretable Machine Learning in Safety Critical Environments.

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• [3]
  

  2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909367

  Kummert, J., et al., 2016. Local Reject Option for Deterministic Multi-class SVM. In A. E.P. Villa, P. Masulli, & A. J. Pons Rivero, eds. Artificial Neural Networks and Machine Learning - ICANN 2016 - 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. Lecture Notes in Computer Science. no.9887 Cham: Springer Nature, pp. 251--258.

  PUB | DOI
   
• [2]
  

  2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900676

  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. Louvain-la-Neuve: Ciaco - i6doc.com, pp. 41--46.

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• [1]
  

  2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905729

  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. Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. Lecture Notes in Computer Science. no.9887 Cham: Springer Nature, pp. 510-517.

  PUB | PDF | DOI