17 Publikationen
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2023 | Bielefelder E-Dissertation | PUB-ID: 2968265Guiding Information: Supervised Models and their Relationship with DataPUB | PDF | DOI
Göpfert, Christina, Guiding Information: Supervised Models and their Relationship with Data. (). Bielefeld, 2023 -
2022 | Konferenzbeitrag | PUB-ID: 2979000Faster Confidence Intervals for Item Response Theory via an Approximate LikelihoodPUB | DOI | Download (ext.)
Paaßen, Benjamin, Faster Confidence Intervals for Item Response Theory via an Approximate Likelihood. Proceedings of the 15th International Conference on Educational Data Mining (EDM 2022) (). , 2022 -
2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2957385How to Compare Adversarial Robustness of Classifiers from a Global PerspectivePUB | DOI
Risse, Niklas, How to Compare Adversarial Robustness of Classifiers from a Global Perspective. Artificial Neural Networks and Machine Learning – ICANN 2021. 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part I 12891 (). Cham, 2021 -
2021 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2955115Supervised learning in the presence of concept drift: a modelling frameworkPUB | DOI | WoS
Straat, M., Supervised learning in the presence of concept drift: a modelling framework. Neural Computing and Applications (). , 2021 -
2020 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982081Prototype-Based Classifiers in the Presence of Concept Drift: A Modelling FrameworkPUB | DOI
Biehl, Michael, Prototype-Based Classifiers in the Presence of Concept Drift: A Modelling Framework. 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 (). Cham, 2020 -
2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2935925When can unlabeled data improve the learning rate?PUB | PDF | arXiv
Göpfert, Christina, When can unlabeled data improve the learning rate?. Conference on Learning Theory (COLT) (). , 2019 -
2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2935456FRI - Feature Relevance Intervals for Interpretable and Interactive Data ExplorationPUB | PDF | DOI | arXiv
Pfannschmidt, Lukas, FRI - Feature Relevance Intervals for Interpretable and Interactive Data Exploration. (). , 2019 -
2019 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2933715Differential privacy for learning vector quantizationPUB | PDF | DOI | WoS
Brinkrolf, Johannes, Differential privacy for learning vector quantization. Neurocomputing 342 (). , 2019 -
2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2932412Statistical Mechanics of On-Line Learning Under Concept DriftPUB | DOI | WoS | PubMed | Europe PMC
Straat, Michiel, Statistical Mechanics of On-Line Learning Under Concept Drift. ENTROPY 20 (10). , 2018 -
2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2911900Time Series Prediction for Graphs in Kernel and Dissimilarity SpacesPUB | DOI | Download (ext.) | WoS | arXiv
Paaßen, Benjamin, Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces. Neural Processing Letters 48 (2). , 2018 -
2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2915273Interpretation of Linear Classifiers by Means of Feature Relevance BoundsPUB | PDF | DOI | WoS
Göpfert, Christina, Interpretation of Linear Classifiers by Means of Feature Relevance Bounds. Neurocomputing 298 (). , 2018 -
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2908201Feature Relevance Bounds for Linear ClassificationPUB | Dateien verfügbar | Download (ext.)
Göpfert, Christina, Feature Relevance Bounds for Linear Classification. Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (). Louvain-la-Neuve, 2017 -
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2913752Effects of Variability in Synthetic Training Data on Convolutional Neural Networks for 3D Head ReconstructionPUB | PDF | DOI
Göpfert, Jan Philip, Effects of Variability in Synthetic Training Data on Convolutional Neural Networks for 3D Head Reconstruction. 2017 SSCI Proceedings. 2017 IEEE Symposium Series on Computational Intelligence (SSCI) (). Piscataway, NJ, 2017 -
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2915274Analyzing Feature Relevance for Linear Reject Option SVM using Relevance IntervalsPUB | PDF
Göpfert, Christina, Analyzing Feature Relevance for Linear Reject Option SVM using Relevance Intervals. Proceedings of the 2017 NIPS workshop on Transparent and Interpretable Machine Learning in Safety Critical Environments (). , 2017 -
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909367Local Reject Option for Deterministic Multi-class SVMPUB | DOI
Kummert, Johannes, Local Reject Option for Deterministic Multi-class SVM. Artificial Neural Networks and Machine Learning - ICANN 2016 - 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II 9887 (). Cham, 2016 -
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900676Gaussian process prediction for time series of structured dataPUB | PDF
Paaßen, Benjamin, Gaussian process prediction for time series of structured data. Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (). Louvain-la-Neuve, 2016 -
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905729Convergence of Multi-pass Large Margin Nearest Neighbor Metric LearningPUB | PDF | DOI
Göpfert, Christina, Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning. Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II 9887 (). Cham, 2016