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Daniela Hofmann
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2010 – 2019
- 2016
- [b1]Daniela Hofmann:
Learning vector quantization for proximity data. Bielefeld University, 2016 - 2015
- [j3]Daniela Hofmann, Andrej Gisbrecht, Barbara Hammer:
Efficient approximations of robust soft learning vector quantization for non-vectorial data. Neurocomputing 147: 96-106 (2015) - 2014
- [j2]Barbara Hammer, Daniela Hofmann, Frank-Michael Schleif, Xibin Zhu:
Learning vector quantization for (dis-)similarities. Neurocomputing 131: 43-51 (2014) - [j1]Daniela Hofmann, Frank-Michael Schleif, Benjamin Paaßen, Barbara Hammer:
Learning interpretable kernelized prototype-based models. Neurocomputing 141: 84-96 (2014) - [c5]Benoît Frénay, Daniela Hofmann, Alexander Schulz, Michael Biehl, Barbara Hammer:
Valid interpretation of feature relevance for linear data mappings. CIDM 2014: 149-156 - 2013
- [c4]Daniela Hofmann, Barbara Hammer:
Sparse approximations for kernel learning vector quantization. ESANN 2013 - 2012
- [c3]Daniela Hofmann, Barbara Hammer:
Kernel Robust Soft Learning Vector Quantization. ANNPR 2012: 14-23 - [c2]Andrej Gisbrecht, Daniela Hofmann, Barbara Hammer:
Discriminative Dimensionality Reduction Mappings. IDA 2012: 126-138 - [c1]Daniela Hofmann, Andrej Gisbrecht, Barbara Hammer:
Efficient Approximations of Kernel Robust Soft LVQ. WSOM 2012: 183-192
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