PUB - Publikationen an der Universität Bielefeld

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• [17]
  

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

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

  PUB | PDF | DOI
   
• [16]
  

  2022 | Konferenzbeitrag | PUB-ID: 2979000

  Paaßen B, Göpfert C, Pinkwart N. Faster Confidence Intervals for Item Response Theory via an Approximate Likelihood. In: Cristea AI, Brown C, Mitrovic T, Bosch N, eds. Proceedings of the 15th International Conference on Educational Data Mining (EDM 2022). 2022: 555–559.

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

  2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2957385

  Risse N, Göpfert C, Göpfert JP. How to Compare Adversarial Robustness of Classifiers from a Global Perspective. In: Farkaš I, Masulli P, Otte S, Wermter S, 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. Vol 12891. Cham: Springer International Publishing; 2021: 29-41.

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• [14]
  

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

  Straat M, Abadi F, Kan Z, Göpfert C, Hammer B, Biehl M. Supervised learning in the presence of concept drift: a modelling framework. Neural Computing and Applications. 2021.

  PUB | DOI | WoS
   
• [13]
  

  2020 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982081

  Biehl M, Abadi F, Göpfert C, Hammer B. Prototype-Based Classifiers in the Presence of Concept Drift: A Modelling Framework. In: Vellido A, Gibert K, Angulo C, Martín Guerrero JD, 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; 2020: 210-221.

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• [12]
  

  2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2935925

  Göpfert C, Ben-David S, Bousquet O, Gelly S, Tolstikhin I, Urner R. When can unlabeled data improve the learning rate? In: Conference on Learning Theory (COLT). 2019.

  PUB | PDF | arXiv
   
• [11]
  

  2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2935456

  Pfannschmidt L, Göpfert C, Neumann U, Heider D, Hammer B. 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. Differential privacy for learning vector quantization. Neurocomputing. 2019;342:125-136.

  PUB | PDF | DOI | WoS
   
• [9]
  

  2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2932412

  Straat M, Abadi F, Göpfert C, Hammer B, Biehl M. Statistical Mechanics of On-Line Learning Under Concept Drift. ENTROPY. 2018;20(10): 775.

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

  2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2911900

  Paaßen B, Göpfert C, Hammer B. Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces. Neural Processing Letters. 2018;48(2):669-689.

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

  2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2915273

  Göpfert C, Pfannschmidt L, Göpfert JP, Hammer B. Interpretation of Linear Classifiers by Means of Feature Relevance Bounds. Neurocomputing. 2018;298:69-79.

  PUB | PDF | DOI | WoS
   
• [6]
  

  2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2908201

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

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

  2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2913752

  Göpfert JP, Göpfert C, Botsch M, Hammer B. 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; 2017.

  PUB | PDF | DOI
   
• [4]
  

  2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2915274

  Göpfert C, Göpfert JP, Hammer B. 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. 2017.

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• [3]
  

  2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909367

  Kummert J, Paaßen B, Jensen J, Göpfert C, Hammer B. Local Reject Option for Deterministic Multi-class SVM. In: E.P. Villa A, Masulli P, Pons Rivero AJ, 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. Vol 9887. Cham: Springer Nature; 2016: 251--258.

  PUB | DOI
   
• [2]
  

  2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900676

  Paaßen B, Göpfert C, Hammer B. Gaussian process prediction for time series of structured data. In: Verleysen M, ed. Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve: Ciaco - i6doc.com; 2016: 41--46.

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• [1]
  

  2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905729

  Göpfert C, Paaßen B, Hammer B. Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning. In: E.P. Villa A, Masulli P, Pons Rivero AJ, 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. Vol 9887. Cham: Springer Nature; 2016: 510-517.

  PUB | PDF | DOI