1. 2019
  2. Bakker, J., & Bunte, K. (2019). Efficient learning of email similarities for customer support. In M. Verleysen (Ed.), 27th European Symposium on Artificial Neural Networks, ESANN 2019 (pp. 119-124). d-side publishing.
  3. Biehl, M., Caticha, N., Opper, M., & Villmann, T. (2019). Statistical Physics of Learning and Inference. In M. Verleysen (Ed.), Proc. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning : ESANN 2019 Ciaco - i6doc.com.
  4. Biehl, M. (2019). Supervised Learning - An Introduction: Lectures given at the 30th Canary Islands Winter School of Astrophysics. (Machine Learning Reports; Vol. 01/2019). Mittweida, Germany: Machine Learning Reports.
  5. Biehl, M., Abadi, F., Göpfert, C., & Hammer, B. (2019). Prototype-based classifiers in the presence of concept drift: A modelling framework. ArXiv e-prints, 1903.07273 (1903.07273 ), [1903.07273 ].
  6. You, J., Trager, S., & Wilkinson, M. H. F. (Accepted/In press). A Fast, Memory-Efficient Alpha-Tree Algorithm using Flooding and Tree Size Estimation. Paper presented at International Symposium on Mathematical Morphology, Saarbrücken, Germany.
  7. Kuijken, K., Heymans, C., Dvornik, A., Hildebrandt, H., de Jong, J. T. A., Wright, A. H., ... Verdoes Kleijn, G. A. (2019). The fourth data release of the Kilo-Degree Survey: ugri imaging and nine-band optical-IR photometry over 1000 square degrees. Manuscript submitted for publication.
  8. Costa, A. C., Barufaldi, B., Borges, L. R., Biehl, M., Maidment, A. D. A., & Vieira, M. A. C. (2019). Analysis of feature relevance using an image quality index applied to digital mammography. In SPIE Medical Imaging 2019 (Vol. 10948). [109485R] San Diego, CA, USA: Society of Photo-Optical Instrumentation Engineers (SPIE). https://doi.org/10.1117/12.2512975
  9. Pfannschmidt, L., Jakob, J., Biehl, M., Tino, P., & Hammer, B. (2019). Feature Relevance Bounds for Ordinal Regression. ArXiv e-prints.
  10. 2018
  11. Bhole, A., Biehl, M., & Azzopardi, G. (2018). Automatic identification of Holstein cattle using non-invasive computer vision approach. Abstract from FAIR Data Science for Green Life Sciences, Wageningen, Netherlands.
  12. Alsahaf, A., Azzopardi, G., Ducro, B., Veerkamp, R., & Petkov, N. (2018). Predicting slaughter age in pigs using random forest regression. In N. Petkov, N. Strisciuglio, & C. M. Travieso-Gonzalez (Eds.), Applications of Intelligent Systems IOS Press. https://doi.org/10.1093/jas/sky359
  13. You, J., Wilkinson, M. H. F., & Trager, S. (2018). Scalable max-tree and alpha-tree algorithm for high resolution, multispectral, and extreme dynamic range images. Poster session presented at XXX Canary Islands Winter School of Astrophysics, Tenerife, Spain.
  14. Bunte, K., Smith, D. J., Chappell, M. J., Hassan-Smith, Z. K., Tomlinson, J. W., Arlt, W., & Tino, P. (2018). Learning pharmacokinetic models for in vivo glucocorticoid activation. Journal of Theoretical Biology, 455, 222-231. https://doi.org/10.1016/j.jtbi.2018.07.025
  15. Villmann, T., Ravichandran, J. R. D., Saralajew, S., & Biehl, M. (2018). Dropout in Learning Vector Quantization Networks for Regularized Learning and Classification Confidence Estimation. 15-21. Abstract from Mittweida Workshop on Computational Intelligence , Mittweida, Germany.
  16. Biehl, M. (2018). The Statistical Physics of Learning (in a nutshell): News from the stoneage of neural networks. 23-23. Abstract from Mittweida Workshop on Computational Intelligence , Mittweida, Germany. https://doi.org/10.1007/s10618-017-0506-1
  17. DelPozo-Banos, M., John, A., Petkov, N., Berridge, D. M., Southern, K., LLoyd, K., ... Manuel Travieso, C. (2018). Using Neural Networks with Routine Health Records to Identify Suicide Risk: Feasibility Study. JMIR mental health, 5(2), [10144]. https://doi.org/10.2196/10144
  18. Smedinga, R., & Biehl, M. (Eds.) (2018). 15th SC@RUG 2018 proceedings 2017-2018. Rijksuniversiteit Groningen.
  19. Mohammadi, M., Peletier, R., Schleif, F-M., Petkov, N., & Bunte, K. (2018). Globular cluster detection in the Gaia survey. In 26th European Symposium on Artificial Neural Networks, ESANN 2018 d-side publishing.
  20. Nolte, A., Wang, L., & Biehl, M. (2018). Prototype-based analysis of GAMA galaxy catalogue data. In M. Verleysen (Ed.), ESANN, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (Vol. 26, pp. 339-344). Louvain-La-Neuve: Ciaco - i6doc.com.
  21. Biehl, M., Bunte, K., Longo, G., & Tino, P. (2018). Machine Learning and Data Analysis in Astroinformatics. In M. Verleysen (Ed.), ESANN, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (Vol. 26, pp. 307-314). Ciaco - i6doc.com.
  22. Neocleous, A. C., Syngelaki, A., Nicolaides, K. H., & Schizas, C. N. (2018). Two-stage approach for risk estimation of fetal trisomy 21 and other aneuploidies using computational intelligence systems. In 29th World Congress on ultrasound in obstetrics and gynecology (4 ed., pp. 503-508). (Ultrasound in Obstetrics & Gynecology; Vol. 51). Wiley. https://doi.org/10.1002/uog.17558
  23. Schiza, E. (2018). An e-health driven national healthcare ecosystem. [Groningen]: University of Groningen.
  24. Alsahaf, A., Ducro, B., Veerkamp, R., Azzopardi, G., & Petkov, N. (2018). Assigning pigs to uniform target weight groups using machine learning: ProceedingsoftheWorldCongressonGeneticsAppliedtoLivestockProduction,11.112. In World Congress on Genetics Applied to Livestock Production (WCGALP) University of New Zealand.
  25. Buhagiar, J., Strisciuglio, N., Petkov, N., & Azzopardi, G. (2018). Automatic Segmentation of Indoor and Outdoor Scenes from Visual Lifelogging. Paper presented at Applications of Intelligent Systems 2018, Las Palmas de Gran Canaria, Spain.
  26. Strisciuglio, N., Azzopardi, G., & Petkov, N. (2018). Brain-inspired robust delineation operator. In European Conference of Computer Vision (ECCV) 2018: Workshop on Brain-inspired computer vision
  27. Mohammadi, M., Petkov, N., Peletier, R., Bibiloni Serrano, P., & Bunte, K. (2018). Detection of Globular Clusters in the Halo of Milky Way. In N. Petkov, N. Strisciuglio, & C. M. Travieso-González (Eds.), Frontiers in Artificial Intelligence and Applications (FAIA) ( Frontiers in Artificial Intelligence and Applications; Vol. 310). IOS Press. https://doi.org/10.3233/978-1-61499-419-0-27
  28. Wilkinson, M. H. F., & Gazagnes, S. (2018). Distributed Component Forests: ImagesHierarchical Image Representations Suitable for Tera-Scale. In C. Y. Suen (Ed.), Proceedings of the International Conference on Pattern Recognition and Artificial Intelligence (pp. 96-101). Montreal, Canada: CENPARMI, Centre for Pattern Recognition and Machine Intellig ence Concordia University, Montreal, Canada .
  29. Alsahaf, A., Azzopardi, G., & Petkov, N. (2018). Estimation of live muscle scores of pigs with RGB-D images and machine learning. Abstract from FAIR Data Science for Green Life Sciences, Wageningen, Netherlands.
  30. Moolla, A., Taylor, A., Gilligan, L., Boer, J. D., Amin, A., Pavlov, D., ... Tomlinson, J. (2018). Staging of non-alcoholic fatty liver disease through LC-MS/MS analysis of the urinary steroid metabolome. Endocrine Abstracts, 59, [endoabs.59.OC3.3]. https://doi.org/10.1530/endoabs.59.OC3.3
  31. 2017
  32. de Oliveira, G., Bolanos, M., Talavera Martínez, E., Gelonch, O., & Garolera, M. (2017). Serious Games Application for Memory Training Using Egocentric Images. In ICIAP 2017 - New Trends in Image Analysis and Processing (pp. 120-130). Springer.
  33. 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. https://doi.org/10.1109/WSOM.2017.8020020
  34. 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. https://doi.org/10.1109/WSOM.2017.8020027
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