6 Publikationen

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

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6 Publikationen

Alle markieren

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

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