6 Publikationen

Alle markieren

[6]
2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2939517
Pfannschmidt L, Jakob J, Hinder F, Biehl M, Tino P, 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, 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, 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, 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 JP, 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, 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.)
 

Suche

Publikationen filtern

Darstellung / Sortierung

Zitationsstil: bio1

Export / Einbettung

6 Publikationen

Alle markieren

[6]
2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2939517
Pfannschmidt L, Jakob J, Hinder F, Biehl M, Tino P, 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, 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, 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, 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 JP, 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, 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.)
 

Suche

Publikationen filtern

Darstellung / Sortierung

Zitationsstil: bio1

Export / Einbettung