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3 Publikationen

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
 
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904469
Hosseini, B., et al., 2016. Non-Negative Kernel Sparse Coding for the Analysis of Motion Data. In A. E.P. Villa, P. Masulli, & A. Javier Pons Rivero, eds. Artificial Neural Networks and Machine Learning – ICANN 2016. Lecture Notes in Computer Science. no.9887 Cham: Springer, pp. 506-514.
PUB | PDF | DOI | Download (ext.) | arXiv
 
2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2678214
Hofmann, D., et al., 2014. Learning interpretable kernelized prototype-based models. Neurocomputing, 141, p 84-96.
PUB | DOI | Download (ext.) | WoS
 

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