prof. dr hab. Jacek Tabor dr hab. Igor T. Podolak dr Jarek Duda dr Krzysztof Misztal dr hab. Adam Roman dr Marek Śmieja dr Przemysław Spurek dr Marcin Żelawski dr Bartosz Zieliński dr Wojciech M. Czarnecki
mgr Stanisław Jastrzębski mgr Damian Leśniak mgr Maciej J. Mikulski mgr Agnieszka Pocha mgr Igor Sieradzki mgr Magdalena Wiercioch mgr Konrad Żołna mgr Jakub Chłędowski mgr Piotr Kowenzowski mgr Łukasz Maziarka


Igor Podolak, Adam Roman, Marek Szykuła, Bartosz Zieliński A machine learning approach to synchronization of automata, EXPERT SYST APPL (2018), 357-371
Oleksandr Myronov, Marek Śmieja, Jacek Tabor Semi-supervised discriminative clustering with graph regularization, KNOWL-BASED SYST vol. 151 (2018), 24-36


Bernhard C. Geiger, Marek Śmieja Semi-supervised cross-entropy clustering with information bottleneck constraint, INFORM SCIENCES vol. 421 (2017), 245-271
Przemysław Spurek, Marek Śmieja, Jacek Tabor SVM with a neutral class, PATTERN ANAL APPL (2017), 17
Łukasz Struski, Marek Śmieja, Jacek Tabor, Bartosz Zieliński Regression SVM for incomplete data, SCHEDAE INFORMATICAE (2017),
Konrad Kamieniecki, Krzysztof Misztal, Przemysław Spurek, Marek Śmieja, Jacek Tabor R Package CEC , NEUROCOMPUTING vol. 237 (2017), 410–413
Marek Śmieja, Magdalena Wiercioch Constrained clustering with a complex cluster structure, ADV DATA ANAL CLASSI vol. 11/3 (2017), 493-518
Rafał Kafel, Marek Śmieja, Dawid Warszycki Practical application of the Average Information Content Maximization (AIC-MAX) algorithm – selection of the most important structural features for serotonin receptor ligands, MOL DIVERS vol. 21/2 (2017), 407-412


Marek Śmieja, Dawid Warszycki Average Information Content Maximization - a new approach for fingerprint hybridization and reduction, PLOS ONE vol. 11/1 (2016), e0146666
Łukasz Struski, Marek Śmieja, Jacek Tabor Semi-supervised model-based clustering with controlled clusters leakage, EXPERT SYST APPL vol. 85 (2016), 146-157
Szymon Nakoneczny, Marek Śmieja Natural language processing methods in biological activity prediction, PROCEEDINGS OF ECML PKDD WORKSHOP ON MACHINE LEARNING IN LIFE SCIENCES (2016), 25-36
Szymon Nakoneczny, Marek Śmieja, Jacek Tabor Fast entropy clustering of sparse high dimensional binary data, PROCEEDNIGS OF IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN 2016) (2016), 2397-2404


Marek Śmieja, Jacek Tabor Entropy approximation in lossy source coding problem, ENTROPY-SWITZ vol. 17/5 (2015), 3400-3418
Marek Śmieja, Jacek Tabor, Magdalena Wiercioch Probability Index of Metric Correspondence as a measure of visualization reliability, PROCEEDINGS OF ECML PKDD WORKSHOP ON MACHINE LEARNING IN LIFE SCIENCES (2015), 16-27
Marek Śmieja, Magdalena Wiercioch Mixture of metrics optimization for machine learning problems, SCHEDAE INFORMATICAE vol. 24 (2015), 133-142
Marek Śmieja, Jacek Tabor Spherical Wards clustering and generalized Voronoi diagrams, PROCEEDING OF IEEE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS vol. 36678 (2015), 10
Marek Śmieja Weighted approach to general entropy function, IMA J MATH CONTROL I vol. 32/2 (2015), 329-327


Andrzej Bojarski, Marek Śmieja, Jacek Tabor, Dawid Warszycki Asymmetric Clustering Index in a case study of 5-HT1A receptor ligands, PLOS ONE vol. 9(7) (2014), e102069
Marek Śmieja, Jacek Tabor Renyi entropy dimension of the mixture of measures, PROCEEDINGS OF SCIENCE AND INFORMATION CONFERENCE (2014), 685-689
Krzysztof Misztal, Przemysław Spurek, Marek Śmieja Subspaces Clustering Approach to Lossy Image Compression, LECTURE NOTES IN COMPUTER SCIENCE vol. 8838 (2014), 571-579
Wojciech Czarnecki, Igor Podolak Adaptive Active Learning as a Multi-armed Bandit Problem, FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS vol. 263 (2014), 989-990


Igor Podolak, Adam Roman Theoretical foundations and practical results for a hierarchical classifier with overlapping clusters, COMPUT INTELL-US (2013), 357-388
Stanisław Jastrzębski, Igor Podolak Density Invariant Detection of Osteoporosis Using Growing Neural Gas, PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON COMPUTER RECOGNITION SYSTEMS CORES 2013 vol. Springer International Publishing (2013), 629-638
Wojciech Czarnecki, Igor Podolak Machine Learning with Known Input Data Uncertainty Measure, LECTURE NOTES IN COMPUTER SCIENCE (2013), 379-388
Marek Śmieja, Jacek Tabor Image segmentation with use of cross-entropy clustering, ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING vol. 226 (2013), 403-409
IT Podolak, A Roman, Theoretical foundations and experimental results for a hierarchical classifier with overlapping clusters, Computational Intelligence 29 (2), 357-388 (2013)


Marek Śmieja, Jacek Tabor Entropy of the mixture of source and entropy dimension, IEEE T INFORM THEORY vol. 58(5) (2012), 2719-2728
Dariusz Jędrzejczyk, Igor Podolak, Adam Roman Application of Hierarchical Classifier to Minimal Synchronizing Word problem, LECTURE NOTES IN COMPUTER SCIENCE vol. 7267 (2012), 421-429
IT Podolak, Hierarchiczny klasyfikator z nakładającymi się grupami klas, Wydawnictwo Uniwersytetu Jagiellońskiego WUJ (2012)


Stanisław Brodowski, Igor Podolak Hierarchical Estimator, EXPERT SYST APPL vol. 38 (2011), 12237–12248
IT Podolak, A Roman, CORES: fusion of supervised and unsupervised training methods for a multi-class classification problem, Pattern Analysis and Applications 14 (4), 395-413 (2011)
Igor Podolak, Adam Roman Fusion of supervised and unsupervised training methods for a multi-class classification problem, PATTERN ANAL APPL vol. 14(4) (2011), 395-413
Igor Podolak, Adam Roman Risk function estimation for subproblems in a hierarchical classifier, PATTERN RECOGN LETT vol. 32(15) (2011), 2136-2142
Igor Podolak, Adam Roman Risk estimation for hierarchical classifier, LECTURE NOTES IN ARTIFICIAL INTELLIGENCE vol. 6678 (2011), 156-163
Agnieszka Mielniczek (Deszyńska), Igor Podolak, Adam Roman On the number of clusterings in a hierarchical classification model with overlapping clusters, SCHEDAE INFORMATICAE vol. 20 (2011), 137-157
K Bartocha, IT Podolak, Classifier Ensembles for Virtual Concept Drift–The DEnBoost Algorithm, International Conference on Hybrid Artificial Intelligence Systems, 164-171 (2011)


IT Podolak, K Bartocha, A hierarchical classifier with growing neural gas clustering, International Conference on Adaptive and Natural Computing Algorithms, 283-292 (2009)
Igor Podolak, Adam Roman A new notion of weakness in classification theory vol. 57 (2009), "Advances in Intelligent and Soft Computing", Springer Verlag (połaczony z Kluwer Academic Publishing)
Kamil Bartocha, Igor Podolak Hierarchical Classifier with Growing Neural Gas clustering, LECTURE NOTES IN COMPUTER SCIENCE vol. 5495 (2009), 283-292


Igor Podolak Hierarchical classifier with overlapping class groups, EXPERT SYST APPL vol. 1 (2008), 673-682
Bartosz Bierkowski, Piotr Kalita, Igor Podolak, Adam Roman Algorithm for intelligent prediction of requests in business systems, LECTURE NOTES IN COMPUTER SCIENCE vol. 4910 (2008), 696-707


IT Podolak, Hierarchical rules for a hierarchical classifier, International Conference on Adaptive and Natural Computing Algorithms, 749-757 (2007)


H Telega, I Podolak, Hill Crunching Clustered Genetic Search and its Improvements, Wyższa Szkoła Biznesu–National-Louis University (2005)


A Bielecki, IT Podolak, M Bielecka, Neuronal algorithm of nonlinear regression, Schedae Informaticae 13 (2004)
I Podolak, U Demšar, Discovering structure in geographical metadata, Proceedings of the 12th conference in Geoinformatics, 1-7 (2004)


BA Podolak I.T., A neuronal system for phonematic transformation, TASK Quaterly 7 (1), 115-130 (2003)
IT Podolak, A Bielecki, A neural system of phonematic transformation, TASK QUARTERLY 7 (1), 115-130 (2003)


D Xi, IT Podolak, SW Lee, Facial component extraction and face recognition with support vector machines, Automatic Face and Gesture Recognition, 2002. Proceedings. Fifth IEEE… (2002)


A Bielecki, M Bielecka, IT Podolak, Symulacja pewnego nieliniowego modelu ekonometrycznego przy pomocy sieci neuronowych, Przegląd Statystyczny 48 (1-2), 151-159 (2001)


LSW Podolak I.T., Finding input symbol representations for neural networks, 7th Int. Conf. on Neural Informations Processing, 1174-1179 (2000)
IT Podolak, SW Lee, A Bielecki, E Majkut, A hybrid neural system for phonematic transformation, Pattern Recognition, 2000. Proceedings. 15th International Conference on 2… (2000)


IT Podolak, Feedforward neural network's sensitivity to input data representation, Computer physics communications 117 (1-2), 181-188 (1999)


IT Podolak, Functional graph model of a neural network, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 28… (1998)


A Bielecki, IT Podolak, J Wosiek, E Majkut, Phonematic translation of polish texts by the neural network, ACTA PHYSICA POLONICA SERIES B 27, 2253-2264 (1996)

Contact Us

Igor Podolak, PhD
Group of Machine Learning Research
Faculty of Mathematics and Computer Science
ul. Lojasiewicza 6
30-342, Cracow, Poland