Johann Bernoulli Inst. for Math. and CompSc.(Math)

Organisational unit: Research Institute

  1. 2017
  2. Bani, G., Seiffert, U., Biehl, M., & Melchert, F. (2017). Adaptive basis functions for prototype-based classification of functional data. In 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) (pp. 1-8). IEEEXplore. DOI: 10.1109/WSOM.2017.8020020
  3. LeKander, M., Biehl, M., & Vries, H. D. (2017). Empirical evaluation of gradient methods for matrix learning vector quantization. In 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) IEEEXplore. DOI: 10.1109/WSOM.2017.8020027
  4. Villmann, T., Biehl, M., Villmann, A., & Saralajew, S. (2017). Fusion of deep learning architectures, multilayer feedforward networks and learning vector quantizers for deep classification learning. In 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) (pp. 1-8). IEEEXplore. DOI: 10.1109/WSOM.2017.8020009
  5. Straat, M., Kaden, M., Gay, M., Villmann, T., Lampe, A., Seiffert, U., ... Melchert, F. (2017). Prototypes and matrix relevance learning in complex fourier space. In 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) (pp. 1-6). IEEEXplore. DOI: 10.1109/WSOM.2017.8020019
  6. de Oliveira, G., Bolanos, M., Talavera Martínez, E., Gelonch, O., & Garolera, M. (2017). Serious Games Application for Memory Training Using Egocentric Images. In Workshop on Social Signal Processing and Beyond
  7. Silvis, M. H., Remmerswaal, R., & Verstappen, R. W. C. P. (2017). A Framework for the Assessment and Creation of Subgrid-Scale Models for Large-Eddy Simulation. In R. Örlü, A. Talamelli, M. Oberlack, & J. Peinke (Eds.), Progress in Turbulence VII: Proceedings of the iTi Conference in Turbulence 2016 (pp. 133-139). Springer International Publishing. DOI: 10.1007/978-3-319-57934-4_19
  8. Neocleous, A., Neocleous, C. K., Schizas, C. N., Biehl, M., & Petkov, N. (2017). Marker selection for the detection of trisomy 21 using generalized matrix learning vector quantization. In Neural Networks (IJCNN), 2017 International Joint Conference on (pp. 3704-3708). IEEEXplore. DOI: 10.1109/IJCNN.2017.7966322
  9. Valentijn, E. A., Begeman, K., Belikov, A., Boxhoorn, D. R., Brinchmann, J., McFarland, J., ... van Dijk, G. J. W. (2017). Target and (Astro-)WISE technologies - Data federations and its applications. In Astroinformatics 2017 (pp. 333-340). (Proceedings IAU Symposium; Vol. 12, issue S325, Astroinformatics). International Astronomical Union. DOI: 10.1017/S1743921317000254
  10. van der Ree, M., Roerdink, J., Phillips, C., Garraux, G., Salmon, E., & Wiering, M. (2017). Support Vector Components Analysis. In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: ESANN
  11. Arlt, W., Lang, K., Sitch, A. J., Dietz, A. S., Rhayem, Y., Bancos, I., ... Reincke, M. (2017). Steroid metabolome analysis reveals prevalent glucocorticoid excess in primary aldosteronism. JCI Insight, 2(8). DOI: 10.1172/jci.insight.93136
  12. de Jong, J. T. A., Verdoes Kleijn, G. A., Erben, T., Hildebrandt, H., Kuijken, K., Sikkema, G., ... Viola, M. (2017). VizieR Online Data Catalog: KiDS-ESO-DR3 multi-band source catalog (de Jong+, 2017). VizieR On-line Data Catalog, 2347.
  13. Bhanot, G., Biehl, M., Villmann, T., & Zühlke, D. (2017). Biomedical data analysis in translational research: Integration of expert knowledge and interpretable models. In M. Verleysen (Ed.), 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2017 (pp. 177-186). Louvain-la-Neuve, Beligium: Ciaco - i6doc.com.
  14. Ghosh, S., Baranowski, E. S., van Veen, R., de Vries, G-J., Biehl, M., Arlt, W., ... Bunte, K. (2017). Comparison of strategies to learn from imbalanced classes for computer aided diagnosis of inborn steroidogenic disorders. In M. Verleysen (Ed.), 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2017 (pp. 199-205). Ciaco - i6doc.com.
  15. Dijkhuis, T., Otter, R., Velthuijsen, H., & Lemmink, K. A. P. M. (2017). Prediction of Running Injuries from Training Load: a Machine Learning Approach. In Prediction of Running Injuries from Training Load: a Machine Learning Approach.
  16. Okafor, E., Pawara, P., Karaaba, M., Surinta, O., Codreanu, V., Schomaker, L., & Wiering, M. (2017). Comparative study between deep learning and bag of visual words for wild-animal recognition. In 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 (2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016). Institute of Electrical and Electronics Engineers Inc.. DOI: 10.1109/SSCI.2016.7850111
  17. Efstathiou, K., Giacobbe, A., Mardešić, P., & Sugny, D. (2017). Rotation Forms and Local Hamiltonian Monodromy. Journal of Mathematical Physics, 58(2), [022902]. DOI: 10.1063/1.4975215
  18. Dimiccoli, M., Bolanos, M., Talavera Martínez, E., Aghaei, M., Nikolov, S. G., & Radeva, P. (2017). SR-clustering: Semantic regularized clustering for egocentric photo streams segmentation. Computer Vision and Image Understanding, 155, 55-69. DOI: 10.1016/j.cviu.2016.10.005
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