| Entropy of the Mixture of Sources and Entropy Dimension M Smieja, J Tabor Information Theory, IEEE Transactions on, 1-1, 2011 | 17 | 2011 |
| Constrained clustering with a complex cluster structure M Śmieja, M Wiercioch Advances in Data Analysis and Classification 11 (3), 493-518, 2017 | 16 | 2017 |
| Average Information Content Maximization—A New Approach for Fingerprint Hybridization and Reduction M Śmieja, D Warszycki PLoS ONE 11 (1), e0146666, 2016 | 11 | 2016 |
| Asymmetric clustering index in a case study of 5-HT1A receptor ligands M Śmieja, D Warszycki, J Tabor, AJ Bojarski PloS one 9 (7), 2014 | 10 | 2014 |
| Weighted approach to general entropy function M Śmieja IMA Journal of Mathematical Control and Information 32 (2), 329-341, 2015 | 9 | 2015 |
| Semi-supervised cross-entropy clustering with information bottleneck constraint M Śmieja, BC Geiger Information Sciences 421, 254-271, 2017 | 8 | 2017 |
| Semi-supervised model-based clustering with controlled clusters leakage M Śmieja, Ł Struski, J Tabor Expert Systems with Applications 85, 146-157, 2017 | 8 | 2017 |
| Spherical Wards clustering and generalized Voronoi diagrams M Śmieja, J Tabor Data Science and Advanced Analytics (DSAA) 36678, 10, 2015 | 8 | 2015 |
| Image segmentation with use of cross-entropy clustering M Śmieja, J Tabor Proceedings of the 8th International Conference on Computer Recognition …, 2013 | 7 | 2013 |
| Processing of missing data by neural networks M Śmieja, Ł Struski, J Tabor, B Zieliński, P Spurek Advances in Neural Information Processing Systems, 2719-2729, 2018 | 5 | 2018 |
| R package cec P Spurek, K Kamieniecki, J Tabor, K Misztal, M Śmieja Neurocomputing 237, 410-413, 2017 | 5 | 2017 |
| Generalized RBF kernel for incomplete data M Śmieja, Ł Struski, J Tabor, M Marzec Knowledge-Based Systems 173, 150-162, 2019 | 4 | 2019 |
| Semi-supervised discriminative clustering with graph regularization M Śmieja, O Myronov, J Tabor Knowledge-Based Systems 151, 24-36, 2018 | 4 | 2018 |
| Entropy approximation in lossy source coding problem M Śmieja, J Tabor Entropy 17 (5), 3400-3418, 2015 | 4 | 2015 |
| Efficient mixture model for clustering of sparse high dimensional binary data M Śmieja, K Hajto, J Tabor Data Mining and Knowledge Discovery 33 (6), 1583-1624, 2019 | 3 | 2019 |
| Fast independent component analysis algorithm with a simple closed-form solution P Spurek, J Tabor, M Śmieja Knowledge-Based Systems 161, 26-34, 2018 | 3 | 2018 |
| Subspaces clustering approach to lossy image compression P Spurek, M Śmieja, K Misztal IFIP International Conference on Computer Information Systems and Industrial …, 2015 | 3 | 2015 |
| Hypernetwork functional image representation S Klocek, Ł Maziarka, M Wołczyk, J Tabor, J Nowak, M Śmieja International Conference on Artificial Neural Networks, 496-510, 2019 | 2 | 2019 |
| Incomplete data representation for SVM classification L Struski, M Smieja, J Tabor arXiv preprint arXiv:1612.01480, 2016 | 2 | 2016 |
| Fast entropy clustering of sparse high dimensional binary data M Śmieja, S Nakoneczny, J Tabor 2016 International Joint Conference on Neural Networks (IJCNN), 2397-2404, 2016 | 2 | 2016 |
Jacek TaborProfesor informatyki, Uniwersytet JagiellońskiVerified email at ii.uj.edu.pl
