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17 Publikationen

2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2914505
Paaßen, B., et al., 2018. Expectation maximization transfer learning and its application for bionic hand prostheses. Neurocomputing, 298, p 122-133.
PUB | DOI | Download (ext.) | WoS | arXiv
 
2018 | Datenpublikation | PUB-ID: 2919994
Paaßen, B., 2018. Tree Edit Distance Learning via Adaptive Symbol Embeddings. Bielefeld University. doi:10.4119/unibi/2919994
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2018 | Datenpublikation | PUB-ID: 2916990
Paaßen, B., 2018. Median Generalized Learning Vector Quantization for Distance Data. Bielefeld University. doi:10.4119/unibi/2916990
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2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2919844
Paaßen, B., et al., 2018. Tree Edit Distance Learning via Adaptive Symbol Embeddings. In J. Dy & A. Krause, eds. Proceedings of the 35th International Conference on Machine Learning (ICML 2018). Proceedings of Machine Learning Research. no.80 pp. 3973-3982.
PUB | Download (ext.) | arXiv
 
2017 | Datenpublikation | PUB-ID: 2912671
Paaßen, B., & Schulz, A., 2017. Linear Supervised Transfer Learning Toolbox. Bielefeld University. doi:10.4119/unibi/2912671
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2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2783224
Paaßen, B., Mokbel, B., & Hammer, B., 2016. Adaptive structure metrics for automated feedback provision in intelligent tutoring systems. Neurocomputing, 192(SI), p 3-13.
PUB | PDF | DOI | WoS
 
2015 | Bielefelder E-Dissertation | PUB-ID: 2733228
Zhu, X., 2015. Adaptive prototype-based dissimilarity learning, Bielefeld: Universitätsbibliothek Bielefeld.
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2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2772407
Nebel, D., et al., 2015. Median variants of learning vector quantization for learning of dissimilarity data. Neurocomputing, 169(SI), p 295-305.
PUB | DOI | WoS
 
2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2776021
Losing, V., Hammer, B., & Wersing, H., 2015. Interactive Online Learning for Obstacle Classification on a Mobile Robot. Presented at the International Joint Conference on Neural Networks, Killarney, Ireland.
PUB | PDF | DOI
 
2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673554
Mokbel, B., Paaßen, B., & Hammer, B., 2014. Adaptive distance measures for sequential data. In M. Verleysen, ed. ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium: i6doc.com, pp. 265-270.
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2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2710067
Mokbel, B., Paaßen, B., & Hammer, B., 2014. Efficient Adaptation of Structure Metrics in Prototype-Based Classification. In S. Wermter, et al., eds. Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings. Lecture Notes in Computer Science. no.8681 Springer, pp. 571-578.
PUB | PDF | DOI | Download (ext.)
 
2013 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2622151
Giotis, I., et al., 2013. Adaptive Matrices and Filters for Color Texture Classification. Journal Of Mathematical Imaging And Vision, 47(1-2), p 79-92.
PUB | DOI | WoS
 
2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2489405
Bunte, K., et al., 2012. Limited Rank Matrix Learning, discriminative dimension reduction and visualization. Neural Networks, 26, p 159-173.
PUB | DOI | WoS | PubMed | Europe PMC
 
2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1796195
Schneider, P., Biehl, M., & Hammer, B., 2010. Hyperparameter learning in probabilistic prototype-based models. Neurocomputing, 73(7-9), p 1117-1124.
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2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1796189
Bunte, K., et al., 2010. Adaptive local dissimilarity measures for discriminative dimension reduction of labeled data. Neurocomputing, 73(7-9), p 1074-1092.
PUB | DOI | WoS
 
2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1795962
Schneider, P., et al., 2010. Regularization in Matrix Relevance Learning. IEEE Transactions on Neural Networks, 21(5), p 831-840.
PUB | DOI | WoS | PubMed | Europe PMC
 
2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1969815
Denecke, A., et al., 2009. Online figure-ground segmentation with adaptive metrics in Generalized LVQ. Neurocomputing, 72(7-9), p 1470-1482.
PUB | DOI | WoS
 

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