6 Publikationen

Alle markieren

[6]
2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2939517
L. Pfannschmidt, et al., “Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information”, Neurocomputing, 2020.
PUB | DOI | Download (ext.) | arXiv
 
[5]
2020 | Preprint | Entwurf | PUB-ID: 2942271 OA
L. Pfannschmidt and B. Hammer, “Sequential Feature Classification in the Context of Redundancies”, Draft.
PUB | PDF | arXiv
 
[4]
2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2933893
L. Pfannschmidt, et al., “Feature Relevance Bounds for Ordinal Regression”, Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019), M. Verleysen, ed., Louvain-la-Neuve: i6doc, 2019.
PUB | Download (ext.) | arXiv
 
[3]
2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2935456 OA
L. Pfannschmidt, et al., “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, 2019.
PUB | PDF | DOI | arXiv
 
[2]
2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2915273 OA
C. Göpfert, et al., “Interpretation of Linear Classifiers by Means of Feature Relevance Bounds”, Neurocomputing, vol. 298, 2018, pp. 69-79.
PUB | PDF | DOI | WoS
 
[1]
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2908201 OA
C. Göpfert, L. Pfannschmidt, and B. Hammer, “Feature Relevance Bounds for Linear Classification”, Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, M. Verleysen, ed., Louvain-la-Neuve: Ciaco - i6doc.com, 2017, pp.187--192.
PUB | Dateien verfügbar | Download (ext.)
 

Suche

Publikationen filtern

Darstellung / Sortierung

Zitationsstil: ieee

Export / Einbettung

6 Publikationen

Alle markieren

[6]
2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2939517
L. Pfannschmidt, et al., “Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information”, Neurocomputing, 2020.
PUB | DOI | Download (ext.) | arXiv
 
[5]
2020 | Preprint | Entwurf | PUB-ID: 2942271 OA
L. Pfannschmidt and B. Hammer, “Sequential Feature Classification in the Context of Redundancies”, Draft.
PUB | PDF | arXiv
 
[4]
2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2933893
L. Pfannschmidt, et al., “Feature Relevance Bounds for Ordinal Regression”, Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019), M. Verleysen, ed., Louvain-la-Neuve: i6doc, 2019.
PUB | Download (ext.) | arXiv
 
[3]
2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2935456 OA
L. Pfannschmidt, et al., “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, 2019.
PUB | PDF | DOI | arXiv
 
[2]
2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2915273 OA
C. Göpfert, et al., “Interpretation of Linear Classifiers by Means of Feature Relevance Bounds”, Neurocomputing, vol. 298, 2018, pp. 69-79.
PUB | PDF | DOI | WoS
 
[1]
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2908201 OA
C. Göpfert, L. Pfannschmidt, and B. Hammer, “Feature Relevance Bounds for Linear Classification”, Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, M. Verleysen, ed., Louvain-la-Neuve: Ciaco - i6doc.com, 2017, pp.187--192.
PUB | Dateien verfügbar | Download (ext.)
 

Suche

Publikationen filtern

Darstellung / Sortierung

Zitationsstil: ieee

Export / Einbettung