Intelligent Systems

Organisational unit: Research Group

  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. 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
  8. 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
  9. 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.
  10. 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.
  11. 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.
  12. 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
  13. 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. DOI: 10.1007/978-3-319-58163-7_1
  14. 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. DOI: 10.1007/978-3-319-58838-4
  15. 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. DOI: 10.1007/978-3-319-59063-9_12
  16. 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.
  17. 2016
  18. Strisciuglio, N., Vento, M., & Petkov, N. (2016). Bio-Inspired Filters for Audio Analysis. In K. Amunts, L. Grandinetti, T. Lippert, & N. Petkov (Eds.), BrainComp 2015: Brain-Inspired Computing (pp. 101). ( Lecture Notes in Computer Science (LNCS); Vol. 10087). Springer. DOI: 10.1007/978-3-319-50862-7_8
  19. 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. DOI: 10.1007/978-3-319-50862-7_10
  20. Melchert, F., Seiffert, U., & Biehl, M. (2016). Functional Representation of Prototypes in LVQ and Relevance Learning. 165-166. Abstract from BNAIC 2016, Amsterdam, Netherlands.
  21. 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. DOI: 10.4230/DagRep.6.6.88
  22. 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.
  23. Bunte, K., Baranowski, E. S., Arlt, W., & Tino, P. (2016). Relevance Learning Vector Quantization in Variable Dimensional Spaces. In Workshop of the GI-Fachgruppe Neuronale Netze and the German Neural Networks Society in connection to GCPR 2016 (pp. 20-23). Hannover, Germany: LNCS.
  24. 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. DOI: 10.1109/CEC.2016.7743855
  25. 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.
  26. 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.
  27. 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. DOI: 10.1007/978-3-319-28518-4_26
  28. 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. DOI: 10.1007/978-3-319-28518-4_28
  29. Bunte, K., Kaden, M., & Schleif, F-M. (2016). Low-Rank Kernel Space Representations in Prototype Learning. In E. Merenyi, M. J. Mendenhall, & P. O'Driscoll (Eds.), Advances in Self-Organizing Maps and Learning Vector Quantization: Proceedings of the 11th International Workshop WSOM (Vol. 428, pp. 341-353). ( Advances in Intelligent Systems and Computing ; Vol. 428). Houston, Texas, USA. DOI: 10.1007/978-3-319-28518-4_30
  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. DOI: 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. DOI: 10.1007/978-3-319-28518-4_29
  32. Sieberts, S., Zhu, F., Garcia-Garcia, J., Stahl, E., Pratap, A., Pandey, G., ... Mangravite, L. (2016). Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis. Nature Communications. DOI: 10.1038/ncomms12460
  33. Schiza, E. C., Fakas, G. J., Pattichis, C. S., Petkov, N., & Schizas, C. N. (2016). Data Protection Issues of Integrated Electronic Health Records (EHR). In E. Kyriacou, S. Christofides, & C. S. Pattichis (Eds.), XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016 (pp. 781-784). (IFMBE Proceedings; Vol. 57). Springer International Publishing.
  34. Moschini, U. (2016). Efficient morphological tools for astronomical image processing [Groningen]: University of Groningen
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