17 Publikationen

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  • [17]
    2023 | Bielefelder E-Dissertation | PUB-ID: 2968265 OA
    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: 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.)
     
  • [15]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2957385
    Risse N, Göpfert C, Göpfert JP (2021)
    How to Compare Adversarial Robustness of Classifiers from a Global Perspective.
    In: Artificial Neural Networks and Machine Learning – ICANN 2021. 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part I. Farkaš I, Masulli P, Otte S, Wermter S (Eds); Lecture Notes in Computer Science, 12891. Cham: Springer International Publishing: 29-41.
    PUB | DOI
     
  • [14]
    2021 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2955115
    Straat M, Abadi F, Kan Z, Göpfert C, Hammer B, Biehl M (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, Abadi F, Göpfert C, Hammer B (2020)
    Prototype-Based Classifiers in the Presence of Concept Drift: A Modelling Framework.
    In: 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. Vellido A, Gibert K, Angulo C, Martín Guerrero JD (Eds); Advances in Intelligent Systems and Computing, Cham: Springer International Publishing: 210-221.
    PUB | DOI
     
  • [12]
    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2935925 OA
    Göpfert C, Ben-David S, Bousquet O, Gelly S, Tolstikhin I, Urner R (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 OA
    Pfannschmidt L, Göpfert C, Neumann U, Heider D, Hammer B (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 OA
    Brinkrolf J, Göpfert C, Hammer B (2019)
    Differential privacy for learning vector quantization.
    Neurocomputing 342: 125-136.
    PUB | PDF | DOI | WoS
     
  • [9]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2932412
    Straat M, Abadi F, Göpfert C, Hammer B, Biehl M (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): 669-689.
    PUB | DOI | Download (ext.) | WoS | arXiv
     
  • [7]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2915273 OA
    Göpfert C, Pfannschmidt L, Göpfert JP, Hammer B (2018)
    Interpretation of Linear Classifiers by Means of Feature Relevance Bounds.
    Neurocomputing 298: 69-79.
    PUB | PDF | DOI | WoS
     
  • [6]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2908201 OA
    Göpfert C, Pfannschmidt L, Hammer B (2017)
    Feature Relevance Bounds for Linear Classification.
    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: 187--192.
    PUB | Dateien verfügbar | Download (ext.)
     
  • [5]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2913752 OA
    Göpfert JP, Göpfert C, Botsch M, Hammer B (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 OA
    Göpfert C, Göpfert JP, 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. .
    PUB | PDF
     
  • [3]
    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
     
  • [2]
    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
     
  • [1]
    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
     

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