1. 2019
  2. 2018
  3. 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.
  4. 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
  5. 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.
  6. 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
  7. Smedinga, R., & Biehl, M. (Eds.) (2018). 15th SC@RUG 2018 proceedings 2017-2018. Rijksuniversiteit Groningen.
  8. 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.
  9. 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.
  10. Schiza, E. (2018). An e-health driven national healthcare ecosystem. [Groningen]: University of Groningen.
  11. Alsahaf, A., Ducro, B., Veerkamp, R., Azzopardi, G., & Petkov, N. (Accepted/In press). Assigning pigs to uniform target weight groups using machine learning. In World Congress on Genetics Applied to Livestock Production (WCGALP)
  12. 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.
  13. 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
  14. Wilkinson, M. H. F., & Gazagnes, S. (Accepted/In press). 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 .
  15. 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.
  16. 2017
  17. 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.
  18. 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
  19. 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
  20. 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. https://doi.org/10.1109/WSOM.2017.8020009
  21. 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. https://doi.org/10.1109/WSOM.2017.8020019
  22. 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. https://doi.org/10.1109/IJCNN.2017.7966322
  23. 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), [UNSP e93136]. https://doi.org/10.1172/jci.insight.93136
  24. Azzopardi, G., Fernandez-Robles, L., Alegre, E., & Petkov, N. (2017). Increased generalization capability of trainable COSFIRE filters with application to machine vision. In 2016 23rd International Conference on Pattern Recognition, ICPR 2016 (pp. 3356-3361). [7900152] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPR.2016.7900152
  25. 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.
  26. 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.
  27. Wilkinson, M. H. F. (2017). A Guided Tour of Connective Morphology: Concepts, Algorithms, and Applications. In W. G. Kropatsch, N. M. Artner, & I. Janusch (Eds.), Discrete Geometry for Computer Imagery: 20th IAPR International Conference, DGCI 2017, Vienna, Austria, September 19 – 21, 2017, Proceedings (pp. 9-18). ( Lecture Notes in Computer Science ; Vol. 10502). Cham: Springer International Publishing AG. https://doi.org/10.1007/978-3-319-66272-5_2
  28. Biehl, M. (2017). Biomedical Applications of Prototype Based Classifiers and Relevance Learning. In D. Figueiredo, C. Martín-Vide, D. Pratas, & M. A. Vega-Rodríguez (Eds.), International Conference on Algorithms for Computational Biology: 4th International Conference, AlCoB 2017, Aveiro, Portugal, June 5-6, 2017, Proceedings (pp. 3-23). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-58163-7_1
  29. Kazemier, J. J., Ouzounis, G. K., & Wilkinson, M. H. F. (2017). Connected Morphological Attribute Filters on Distributed Memory Parallel Machines. In J. Angulo, S. Velasco-Forero, & F. Meyer (Eds.), Mathematical Morphology and Its Applications to Signal and Image Processing: 13th International Symposium, ISMM 2017, Fontainebleau, France, May 15–17, 2017, Proceedings (pp. 357-368). (Image Processing, Computer Vision, Pattern Recognition, and Graphics; Vol. 10225). Cham: Springer International Publishing AG. https://doi.org/10.1007/978-3-319-57240-6
  30. Strisciuglio, N., Azzopardi, G., & Petkov, N. (2017). Detection of curved lines with B-COSFIRE filters: A case study on crack delineation. In Computer Analysis of Images and Patterns - 17th International Conference, CAIP 2017, Proceedings (Vol. 10424 LNCS, pp. 108-120). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10424 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-64689-3_9
  31. Bracci, F., Hillenbrand, U., Marton, Z-C., & Wilkinson, M. H. F. (2017). On the Use of the Tree Structure of Depth Levels for Comparing 3D Object Views. In M. Felsberg, A. Heyden, & N. Krüger (Eds.), Computer Analysis of Images and Patterns: 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part II (pp. 251-263). ( Image Processing, Computer Vision, Pattern Recognition, and Graphics ; Vol. 10425). Cham: Springer International Publishing AG. https://doi.org/10.1007/978-3-319-64698-5
  32. Talavera Martínez, E., Strisciuglio, N., Petkov, N., & Radeva, P. (2017). Sentiment Recognition in Egocentric Photostreams. In L. A. Alexandre, J. Salvador Sanchez, & J. M. F. Rodrigues (Eds.), Pattern Recognition and Image Analysis: Proceedings ( Lecture Notes in Computer Science ; Vol. 10255). Springer. https://doi.org/10.1007/978-3-319-58838-4
  33. Mohammadi, M., Biehl, M., Villmann, A., & Villmann, T. (2017). Sequence Learning in Unsupervised and Supervised Vector Quantization Using Hankel Matrices. In L. Rutkowski (Ed.), International Conference on Artificial Intelligence and Soft Computing: ICAISC 2017 (pp. 131-142). (Lecture Notes in Computer Science; Vol. 10245). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-59063-9_12
  34. Talavera Martínez, E., Strisciuglio, N., Radeva, P., & Petkov, N. (2017). Towards Egocentric Sentiment Analysis. Paper presented at Sixteenth International Conference on Computer Aided Systems Theory, Las Palmas, Spain.
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