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3 Publikationen

2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2933893
Pfannschmidt, Lukas, Jakob, Jonathan, Biehl, Michael, Tino, Peter, and Hammer, Barbara. “Feature Relevance Bounds for Ordinal Regression”. Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019). Ed. Michel Verleysen. Louvain-la-Neuve: i6doc, 2019.
PUB | Download (ext.) | arXiv
 
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904469
Hosseini, Babak, Hülsmann, Felix, Botsch, Mario, and Hammer, Barbara. “Non-Negative Kernel Sparse Coding for the Analysis of Motion Data”. Artificial Neural Networks and Machine Learning – ICANN 2016. Ed. Alessandro E.P. Villa, Paolo Masulli, and Antonio Javier Pons Rivero. Cham: Springer, 2016.Vol. 9887. Lecture Notes in Computer Science. 506-514.
PUB | PDF | DOI | Download (ext.) | arXiv
 
2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2678214
Hofmann, Daniela, Schleif, Frank-Michael, Paaßen, Benjamin, and Hammer, Barbara. “Learning interpretable kernelized prototype-based models”. Neurocomputing 141 (2014): 84-96.
PUB | DOI | Download (ext.) | WoS
 

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