Daniela Hofmann
PEVZ-ID
13 Publikationen
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2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900320Frenay, B., Hofmann, D., Schulz, A., Biehl, M., and Hammer, B. (2014). “Valid interpretation of feature relevance for linear data mappings” in 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) (Piscataway, NJ: Institute of Electrical & Electronics Engineers (IEEE), 149-156.PUB | PDF | DOI
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2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2678214Hofmann, D., Schleif, F. - M., Paaßen, B., and Hammer, B. (2014). Learning interpretable kernelized prototype-based models. Neurocomputing 141, 84-96.PUB | DOI | Download (ext.) | WoS
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2013 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982102Hofmann, D., Gisbrecht, A., and Hammer, B. (2013). “Efficient Approximations of Kernel Robust Soft LVQ” in Advances in Self-Organizing Maps, Estévez, P. A., Príncipe, J. C., and Zegers, P. eds. Advances in Intelligent Systems and Computing (Berlin, Heidelberg: Springer Berlin Heidelberg), 183-192.PUB | DOI
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2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625199Hofmann, D., and Hammer, B. (2013). “Sparse approximations for kernel learning vector quantization” in ESANN.PUB
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2012 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982106Gisbrecht, A., Hofmann, D., and Hammer, B. (2012). “Discriminative Dimensionality Reduction Mappings” in Advances in Intelligent Data Analysis XI, Hollmén, J., Klawonn, F., and Tucker, A. eds. Lecture Notes in Computer Science (Berlin, Heidelberg: Springer Berlin Heidelberg), 126-138.PUB | DOI
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2012 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982107Hofmann, D., and Hammer, B. (2012). “Kernel Robust Soft Learning Vector Quantization” in Artificial Neural Networks in Pattern Recognition, Mana, N., Schwenker, F., and Trentin, E. eds. Lecture Notes in Computer Science (Berlin, Heidelberg: Springer Berlin Heidelberg), 14-23.PUB | DOI
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2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2671172Hofmann, D., Gisbrecht, A., and Hammer, B. (2012). “Discriminative probabilistic prototype based models in kernel space” in Workshop NC^2 2012 (TR Machine Learning Reports).PUB
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2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625238Hofmann, D., Gisbrecht, A., and Hammer, B. (2012). “Efficient Approximations of Kernel Robust Soft LVQ” in WSOM.PUB
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2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625247Gisbrecht, A., Hofmann, D., and Hammer, B. (2012). “Discriminative Dimensionality Reduction Mappings” in Advances in Intelligent Data Analysis XI - 11th International Symposium, IDA 2012, Helsinki, Finland, October 25-27, 2012. Proceedings, Hollmén, J., Klawonn, F., and Tucker, A. eds. Lecture Notes in Computer Science, vol. 7619, (Springer), 126-138.PUB
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2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625254Hofmann, D., and Hammer, B. (2012). “Kernel Robust Soft Learning Vector Quantization” in Artificial Neural Networks in Pattern Recognition - 5th INNS IAPR TC 3 GIRPR Workshop, ANNPR 2012, Trento, Italy, September 17-19, 2012. Proceedings, Mana, N., Schwenker, F., and Trentin, E. eds. Lecture Notes in Computer Science, vol. 7477, (Springer), 14-23.PUB