6 Publikationen

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[6]
2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2939517
Pfannschmidt, L., Jakob, J., Hinder, F., Biehl, M., Tino, P., and Hammer, B. (2020). Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information. Neurocomputing.
PUB | DOI | Download (ext.) | arXiv
 
[5]
2020 | Preprint | Entwurf | PUB-ID: 2942271 OA
Pfannschmidt, L., and Hammer, B. (Draft). Sequential Feature Classification in the Context of Redundancies.
PUB | PDF | arXiv
 
[4]
2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2933893
Pfannschmidt, L., Jakob, J., Biehl, M., Tino, P., and Hammer, B. (2019). “Feature Relevance Bounds for Ordinal Regression” in Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019), Verleysen, M. ed. ( Louvain-la-Neuve: i6doc).
PUB | Download (ext.) | arXiv
 
[3]
2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2935456 OA
Pfannschmidt, L., Göpfert, C., Neumann, U., Heider, D., and 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
 
[2]
2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2915273 OA
Göpfert, C., Pfannschmidt, L., Göpfert, J. P., and Hammer, B. (2018). Interpretation of Linear Classifiers by Means of Feature Relevance Bounds. Neurocomputing 298, 69-79.
PUB | PDF | DOI | WoS
 
[1]
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2908201 OA
Göpfert, C., Pfannschmidt, L., and 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.)
 

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6 Publikationen

Alle markieren

[6]
2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2939517
Pfannschmidt, L., Jakob, J., Hinder, F., Biehl, M., Tino, P., and Hammer, B. (2020). Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information. Neurocomputing.
PUB | DOI | Download (ext.) | arXiv
 
[5]
2020 | Preprint | Entwurf | PUB-ID: 2942271 OA
Pfannschmidt, L., and Hammer, B. (Draft). Sequential Feature Classification in the Context of Redundancies.
PUB | PDF | arXiv
 
[4]
2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2933893
Pfannschmidt, L., Jakob, J., Biehl, M., Tino, P., and Hammer, B. (2019). “Feature Relevance Bounds for Ordinal Regression” in Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019), Verleysen, M. ed. ( Louvain-la-Neuve: i6doc).
PUB | Download (ext.) | arXiv
 
[3]
2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2935456 OA
Pfannschmidt, L., Göpfert, C., Neumann, U., Heider, D., and 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
 
[2]
2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2915273 OA
Göpfert, C., Pfannschmidt, L., Göpfert, J. P., and Hammer, B. (2018). Interpretation of Linear Classifiers by Means of Feature Relevance Bounds. Neurocomputing 298, 69-79.
PUB | PDF | DOI | WoS
 
[1]
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2908201 OA
Göpfert, C., Pfannschmidt, L., and 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.)
 

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