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21 Publikationen

2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2914505
B. Paaßen, et al., “Expectation maximization transfer learning and its application for bionic hand prostheses”, Neurocomputing, vol. 298, 2018, pp. 122-133.
PUB | DOI | Download (ext.) | WoS | arXiv
 
2018 | Datenpublikation | PUB-ID: 2919994
B. Paaßen, Tree Edit Distance Learning via Adaptive Symbol Embeddings. Bielefeld University, 2018. doi:10.4119/unibi/2919994.
PUB | Dateien verfügbar | DOI
 
2018 | Datenpublikation | PUB-ID: 2916990
B. Paaßen, Median Generalized Learning Vector Quantization for Distance Data. Bielefeld University, 2018. doi:10.4119/unibi/2916990.
PUB | Dateien verfügbar | DOI
 
2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2919844
B. Paaßen, et al., “Tree Edit Distance Learning via Adaptive Symbol Embeddings”, Proceedings of the 35th International Conference on Machine Learning (ICML 2018), J. Dy and A. Krause, eds., Proceedings of Machine Learning Research, vol. 80, 2018, pp.3973-3982.
PUB | Download (ext.) | arXiv
 
2017 | Datenpublikation | PUB-ID: 2912671
B. Paaßen and A. Schulz, Linear Supervised Transfer Learning Toolbox. Bielefeld University, 2017. doi:10.4119/unibi/2912671.
PUB | Dateien verfügbar | DOI
 
2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2783224
B. Paaßen, B. Mokbel, and B. Hammer, “Adaptive structure metrics for automated feedback provision in intelligent tutoring systems”, Neurocomputing, vol. 192, 2016, pp. 3-13.
PUB | PDF | DOI | WoS
 
2015 | Bielefelder E-Dissertation | PUB-ID: 2733228
X. Zhu, Adaptive prototype-based dissimilarity learning, Bielefeld: Universitätsbibliothek Bielefeld, 2015.
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2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2772407
D. Nebel, et al., “Median variants of learning vector quantization for learning of dissimilarity data”, Neurocomputing, vol. 169, 2015, pp. 295-305.
PUB | DOI | WoS
 
2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2776021
V. Losing, B. Hammer, and H. Wersing, “Interactive Online Learning for Obstacle Classification on a Mobile Robot”, Presented at the International Joint Conference on Neural Networks, Killarney, Ireland, IEEE, 2015.
PUB | PDF | DOI
 
2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673554
B. Mokbel, B. Paaßen, and B. Hammer, “Adaptive distance measures for sequential data”, ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, M. Verleysen, ed., Bruges, Belgium: i6doc.com, 2014, pp.265-270.
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2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2710067
B. Mokbel, B. Paaßen, and B. Hammer, “Efficient Adaptation of Structure Metrics in Prototype-Based Classification”, Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings, S. Wermter, et al., eds., Lecture Notes in Computer Science, vol. 8681, Springer, 2014, pp.571-578.
PUB | PDF | DOI | Download (ext.)
 
2013 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2622151
I. Giotis, et al., “Adaptive Matrices and Filters for Color Texture Classification”, Journal Of Mathematical Imaging And Vision, vol. 47, 2013, pp. 79-92.
PUB | DOI | WoS
 
2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2509858
M. Kaestner, et al., “Functional relevance learning in generalized learning vector quantization”, Neurocomputing, vol. 90, 2012, pp. 85-95.
PUB | DOI | WoS
 
2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2489405
K. Bunte, et al., “Limited Rank Matrix Learning, discriminative dimension reduction and visualization”, Neural Networks, vol. 26, 2012, pp. 159-173.
PUB | DOI | WoS | PubMed | Europe PMC
 
2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1796195
P. Schneider, M. Biehl, and B. Hammer, “Hyperparameter learning in probabilistic prototype-based models”, Neurocomputing, vol. 73, 2010, pp. 1117-1124.
PUB | DOI | WoS
 
2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1796189
K. Bunte, et al., “Adaptive local dissimilarity measures for discriminative dimension reduction of labeled data”, Neurocomputing, vol. 73, 2010, pp. 1074-1092.
PUB | DOI | WoS
 
2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1795962
P. Schneider, et al., “Regularization in Matrix Relevance Learning”, IEEE Transactions on Neural Networks, vol. 21, 2010, pp. 831-840.
PUB | DOI | WoS | PubMed | Europe PMC
 
2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1969815
A. Denecke, et al., “Online figure-ground segmentation with adaptive metrics in Generalized LVQ”, Neurocomputing, vol. 72, 2009, pp. 1470-1482.
PUB | DOI | WoS
 
2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1969830
S. Kirstein, et al., “A Vision Architecture for Unconstrained and Incremental Learning of Multiple Categories”, Memetic Computing, vol. 1, 2009, pp. 291-304.
PUB | DOI
 
2001 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1617662
G. Heidemann and H. Ritter, “Efficient vector quantization using the WTA-rule with activity equalization”, Neural Processing Letters, vol. 13, 2001, pp. 17-30.
PUB | DOI | WoS
 

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