prof. dr. M. (Michael) Biehl

Adjunct Hoogleraar (tenured, with ius promovendi)

Research

Research units:

Postal address:
Nijenborgh
9
Gebouw 5161, ruimte 0584
Groningen
Netherlands
Phone: +31 50 363 3997
  1. 2018
  2. 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.
  3. 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.
  4. 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
  5. Smedinga, R., & Biehl, M. (Eds.) (2018). 15th SC@RUG 2018 proceedings 2017-2018. Rijksuniversiteit Groningen.
  6. 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.
  7. 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.
  8. van Veen, R., Talavera Martinez, L., Kogan, R. V., Meles, S., Mudali, D., Roerdink, J. B. T. M., ... Biehl, M. (2018). Machine Learning Based Analysis of FDG-PET Image Data for the Diagnosis of Neurodegenerative Diseases. In N. Petkov, N. Strisciuglio, & C. Travieso-González (Eds.), Volume 310: Applications of Intelligent Systems (Vol. 310, pp. 280-289). (Frontiers in Artificial Intelligence and Applications).
  9. 2017
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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.
  17. 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.
  18. 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
  19. 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
  20. 2016
  21. Biehl, M., Mudali, D., Leenders, K. L., & Roerdink, J. B. T. M. (2016). Classification of FDG-PET Brain Data by Generalized Matrix Relevance LVQ. In K. Amunts, L. Grandinetti, T. Lippert, & N. Petkov (Eds.), Brain-Inspired Computing: Second International Workshop, BrainComp 2015, Cetraro, Italy, July 6-10, 2015, Revised Selected Papers (pp. 131-141). (Lecture Notes in Computer Science; Vol. 10087). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-50862-7_10
  22. Melchert, F., Seiffert, U., & Biehl, M. (2016). Functional Representation of Prototypes in LVQ and Relevance Learning. 165-166. Abstract from BNAIC 2016, Amsterdam, Netherlands.
  23. Bhanot, G., Biehl, M., Villmann, T., & Zuehlke, D. (2016). Integration of Expert Knowledge for Interpretable Models in Biomedical Data Analysis (Dagstuhl Seminar 16261): Dagstuhl Reports. (Dagstuhl Reports; Vol. 6, No. 6). Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany. https://doi.org/10.4230/DagRep.6.6.88
  24. Melchert, F., Seiffert, U., & Biehl, M. (2016). Functional approximation for the classification of smooth time series. In B. Hammer, T. Martinetz, & T. Villmann (Eds.), Workshop New Challenges in Neural Computation 2016 (pp. 24-31). (Machine Learning Reports; Vol. 04/2016). Bielefeld: Univ. of Bielefeld.
  25. Mukherjee, G., Bhanot, G., Raines, K., Sastry, S., Doniach, S., & Biehl, M. (2016). Predicting recurrence in clear cell Renal Cell Carcinoma: Analysis of TCGA data using outlier analysis and generalized matrix LVQ. In 2016 IEEE Congress on Evolutionary Computation (CEC) (pp. 656-661). IEEEXplore. https://doi.org/10.1109/CEC.2016.7743855
  26. Melchert, F., Matros, A., Biehl, M., & Seiffert, U. (2016). The sugar dataset: A multimodal hyperspectral dataset for classification and research. In F-M. Schleif, & T. Villmann (Eds.), Machine Learning Reports: MIWOCI Workshop 2016 (Vol. 03, pp. 15). Bielefeld: Univ. of Bielefeld.
  27. Melchert, F., Seiffert, U., & Biehl, M. (2016). Funktionale Approximation von Spektraldaten zur Steigerung der Klassifikationleistung in GMLVQ. In M. Schenk (Ed.), 17. Forschungskolloquium am Fraunhofer IFF 2015 (pp. 49-54). Magdeburg, Germany: Fraunhofer-Institut für Fabrikbetrieb und Automatisierung IFF.
  28. Gay, M., Kaden, M., Biehl, M., Lampe, A., & Villmann, T. (2016). Complex Variants of GLVQ Based on Wirtinger’s Calculus. In E. Merényi, M. J. Mendenhall, & P. O'Driscoll (Eds.), Advances in Self-Organizing Maps and Learning Vector Quantization (Vol. 428, pp. 293-303). (Advances in Intelligent Systems and Computing). Springer. https://doi.org/10.1007/978-3-319-28518-4_26
  29. Melchert, F., Seiffert, U., & Biehl, M. (2016). Functional Representation of Prototypes in LVQ and Relevance Learning. In E. Merényi, M. J. Mendenhall, & P. O'Driscoll (Eds.), Advances in Self-Organizing Maps and Learning Vector Quantization (Vol. 428, pp. 317-327). (Advances in Intelligent Systems and Computing). Springer. https://doi.org/10.1007/978-3-319-28518-4_28
  30. Mudali, D., Biehl, M., Leenders, K., & Roerdink, J. (2016). LVQ and SVM Classification of FDG-PET Brain Data. In E. Merényi, M. J. Mendenhall, & P. O'Driscoll (Eds.), Advances in Self-Organizing Maps and Learning Vector Quantization (Vol. 428, pp. 205-215). (Advances in Intelligent Systems and Computing). Springer. https://doi.org/10.1007/978-3-319-28518-4_18
  31. Mwebaze, E., & Biehl, M. (2016). Prototype-Based Classification for Image Analysis and Its Application to Crop Disease Diagnosis. In E. Merényi, M. J. Mendenhall, & P. O'Driscoll (Eds.), Advances in Self-Organizing Maps and Learning Vector Quantization (Vol. 428, pp. 329-339). (Advances in Intelligent Systems and Computing). Springer. https://doi.org/10.1007/978-3-319-28518-4_29
  32. Moolla, A., Amin, A., Hughes, B. A., Arlt, W., Hassan-Smith, Z., Armstrong, M., ... Tomlinson, J. (2016). The changing ‘steroid metabolome’ across the spectrum of non-alcoholic fatty liver disease. https://doi.org/10.1530/endoabs.41.GP173
  33. Moolla, A., Amin, A., Hughes, B. A., Arlt, W., Hassan-Smith, Z., Armstrong, M., ... Tomlinson, J. (2016). The urinary steroid metabolome as a non-invasive tool to stage non-alcoholic fatty liver disease. https://doi.org/10.1530/endoabs.44.OC1.4
  34. de Vries, G-J., Lemmens, P., Brokken, D., Pauws, S. C. S., & Biehl, M. (2016). Towards Emotion Classification Using Appraisal Modeling. In Psychology and Mental Health: Concepts, Methodologies, Tools, and Applications (pp. 552-572). IGI Global. https://doi.org/10.4018/978-1-5225-0159-6.ch023
  35. Smedinga, R., Biehl, M., & Kramer, F. (Eds.) (2016). 13th SC@RUG 2016 proceedings 2015-2016. Groningen: Rijksuniversiteit Groningen.
  36. 2015
  37. Schulz, A., Mokbel, B., Biehl, M., & Hammer, B. (2015). Inferring Feature Relevances From Metric Learning. In Computational Intelligence, 2015 IEEE Symposium Series on (pp. 1599-1606). IEEE (The Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/SSCI.2015.225
  38. Melchert, F., Seiffert, U., & Biehl, M. (2015). Polynomial Approximation of Spectral Data in LVQ and Relevance Learning. In B. Hammer, T. Martinetz, & T. Villmann (Eds.), Workshop on New Challenges in Neural Computation 2015 (pp. 25-32). (Machine Learning Reports; Vol. 03-2015).
  39. Taylor, A. E., Bancos, I., Chortis, V., Lang, K., O'Neil, D. M., Hughes, B. A., ... Arlt, W. (2015). Further advances in diagnosis of adrenal cancer: a high-throughput urinary steroid profiling method using liquid chromatography tandem mass spectrometry (LC-MS/MS). Abstract from Society for Endocrinology BES 2015, Edinburgh, United Kingdom. https://doi.org/10.1530/endoabs.38.OC2.3
  40. Lang, K., Beuschlein, F., Biehl, M., Dietz, A., Riester, A., Hughes, B. A., ... Arlt, W. (2015). Urine steroid metabolomics as a diagnostic tool in primary aldosteroinism. Abstract from Society for Endocrinology BES 2015, Edinburgh, United Kingdom. https://doi.org/10.1530/endoabs.38.OC1.6
  41. Chortis, V., Bancos, I., Lang, K., Hughes, B. A., O'Neil, D. M., Taylor, A. E., ... Arlt, W. (2015). Urine steroid metabolomics as a novel diagnostic tool for early detection of recurrence in adrenocortical carcinoma. Abstract from Society for Endocrinology BES 2015, Edinburgh, United Kingdom. https://doi.org/10.1530/endoabs.38.OC3.4
Previous 1 2 3 4 5 6 Next

ID: 239697