Publications - Jiří Grim


Books and chapters (4)

1. Jiří Grim, Petr Somol: A Statistical Review of the MNIST Benchmark Data Problem. Advances in Pattern Recognition Research, 172-193. Nova Science Publishers, Inc., New York 2018.   Download
2. * Igor Vajda, Jiří Grim: Neural networks. Systems Science and Cybernetics, 224-248. Eolss Publishers-UNESCO, Oxford 2008.   Download
3. * Jiří Grim: Pravděpodobnostní neuronové sítě. Umělá inteligence (4), 276-312. Academia, Praha 2003.
4. * Jiří Grim, Pavel Boček, Pavel Pudil: Interaktivní prezentace výsledků sčítání lidu pomocí pravděpodobnostních modelů se zaručenou ochranou anonymity dat. Manažerské rozhledy FM 2001. Sborník příspěvků, 13-18. VŠE, Jindřichův Hradec 2002.

Journal articles (32)

1. Dalibor Miklík, Jiří Grim, Daniel Elleder, Jiří Hejnar: Unraveling the palindromic and nonpalindromic motifs of retroviral integration site sequences by statistical mixture models. Genome Research 33:8 (2023), 1395-1408. Cold Spring Harbor Laboratory Press.   Download
2. Jiří Grim: Approximation of Unknown Multivariate Probability Distributions by Using Mixtures of Product Components: A Tutorial. International Journal of Pattern Recognition and Artificial Intelligence 31.   Download
3. Jiří Grim: Sequential pattern recognition by maximum conditional informativity. Pattern Recognition Letters 45:1 (2014), 39-45. Elsevier.   Download
4. Petr Somol, Jiří Grim, Jana Novovičová, P. Pudil: Improving feature selection process resistance to failures caused by curse-of-dimensionality effects. Kybernetika 47:3 (2011), 401-425. Ústav teorie informace a automatizace AV ČR, v. v. i..   Download
5. Michal Haindl, Vojtěch Havlíček, Jiří Grim: Probabilistic mixture-based image modelling. Kybernetika 47:3 (2011), 482-500. Ústav teorie informace a automatizace AV ČR, v. v. i..   Download
6. Jiří Grim, Jan Hora, Pavel Boček, Petr Somol, Pavel Pudil: Statistical Model of the 2001 Czech Census for Interactive Presentation. Journal of Official Statistics 4 (2010), 1-23.   Download
7. * Jiří Grim, Petr Somol, Michal Haindl, J. Daneš: Computer-Aided Evaluation of Screening Mammograms Based on Local Texture Models. IEEE Transactions on Image Processing 18:4 (2009), 765-773. Institute of Electrical and Electronics Engineers.
8. * Jiří Grim, J. Hora, Petr Somol, Pavel Boček, P. Pudil: Interaktivní statistický model dat ze sčítání lidu v České republice v r. 2001. Statistika: Statistics and Economy Journal 89:4 (2009), 285-299. Český Statistický Úřad.   Download
9. * Jiří Grim, Jan Hora: Iterative principles of recognition in probabilistic neural networks. Neural Networks 21:6 (2008), 838-846. Elsevier.
10. * Michal Haindl, Vojtěch Havlíček, Jiří Grim: Probabilistic Discrete Mixtures Colour Texture Models. Lecture Notes in Computer Science 5197 (2008), 675-682.   Download
11. * Michal Haindl, Jiří Grim, Stanislav Mikeš: Texture Defect Detection. Lecture Notes in Computer Science 2007, 987-994.   Download
12. * Jiří Grim: Neuromorphic features of probabilistic neural networks. Kybernetika 43:5 (2007), 697-712. Ústav teorie informace a automatizace AV ČR, v. v. i..
13. * Jiří Grim, Jan Hora: Minimum Information Loss Cluster Analysis for Cathegorical Data. Lecture Notes in Computer Science 2007, 233-247.
14. * Jiří Grim: EM cluster analysis for categorical data. Lecture Notes in Computer Science 44:4109 (2006), 640-648.
15. * Jiří Grim, Petr Somol, Michal Haindl, Pavel Pudil: Color Texture Segmentation by Decomposition of Gaussian Mixture Model. Lecture Notes in Computer Science 19:4225 (2006), 287-296.   Download
16. * Jiří Grim, Petr Somol, Pavel Pudil: Probabilistic neural network playing and learning Tic-Tac-Toe. Pattern Recognition Letters 26:12 (2005), 1866-1873. Elsevier.
17. * Jiří Grim, J. Hora, Pavel Pudil: Interaktivní reprodukce výsledků sčítání lidu pomocí statistického modelu se zaručenou ochranou anonymity dat. Statistika: Statistics and Economy Journal 40:5 (2004), 400-414. Český Statistický Úřad.
18. * Jiří Grim, Michal Haindl: Texture modelling by discrete distribution mixtures. Computational Statistics and Data Analysis 41, 603-615. Elsevier.
19. * Jiří Grim, P. Just, Pavel Pudil: Strictly modular probabilistic neural networks for pattern recognition. Neural Network World 13:6 (2003), 599-615. Ústav informatiky AV ČR, v. v. i..
20. * Jiří Grim, J. Kittler, Pavel Pudil, Petr Somol: Multiple classifier fusion in probabilistic neural networks. Pattern Analysis and Applications 5:7 (2002), 221-233.
21. * Jiří Grim, Pavel Pudil, Petr Somol: Probabilistic information retrieval from census data based on distribution mixtures. Acta Oeconomica Pragensia 8:2 (2000), 41-47.
22. * Jiří Grim: Self-organizing maps and probabilistic neural networks. Neural Network World 10:3 (2000), 407-415. Ústav informatiky AV ČR, v. v. i..
23. * Jiří Grim, J. Vejvalková: An iterative inference mechanism for the probabilistic expert system PES. International Journal of General Systems 27, 373-396. Taylor & Francis.
24. * Igor Vajda, Jiří Grim: About the maximum information and maximum likelihood principles in neural networks. Kybernetika 34:4 (1998), 485-494. Ústav teorie informace a automatizace AV ČR, v. v. i..
25. * Jiří Grim: Mixture of experts architectures for neural networks as a special case of conditional expectation formula. Kybernetika 34:4 (1998), 417-422. Ústav teorie informace a automatizace AV ČR, v. v. i..
26. * Jiří Grim: Knowledge representation and uncertainty processing in the probabilistic expert system PES. International Journal of General Systems 22:2 (1994), 103-111. Taylor & Francis.
27. * Pavel Boček, Jiří Grim, Pavel Kolář: Kam s kupóny v druhém kole?. Hospodářské noviny 135 (1992), 8.
28. * Jiří Grim, Pavel Kolář: Kuponová privatizace: odhadování rizika převisů. Akcionář 3:21 (1992), 10-11.
29. * Pavel Boček, Jiří Grim, Pavel Kolář: Podle počítače. Porovnání výkonnosti českých a slovenských podniků, zařazených do první vlny kupónové privatizace. Hospodářské noviny 115 (1992), 8.
30. * Pavel Boček, Jiří Grim, Pavel Kolář: Kupónová privatizace zdárně pokračuje. Akcionář 3:15 (1992), 8.
31. * Jiří Grim, Pavel Kolář: Akciové společnosti s téměř jistým převisem poptávky ve 4. kole. Hospodářské noviny 209 (1992), 8.
32. * Jiří Grim: Probabilistic expert systems and distribution mixtures. Computers and Artificial Intelligence 9:3 (1990), 241-256.

Conference papers (52)

1. Jiří Grim: Feasibility Study of an Interactive Medical Diagnostic Wikipedia. SPMS 2016 Stochastic and Physical Monitoring Systems, 31-45. Czech Technical University, Prague 2016.   Download
2. Jiří Grim, P. Pudil: Mixtures of Product Components versus Mixtures of Dependence Trees. Computational Intelligence, 365-382. Springer, Cham 2016.   Download
3. Jiří Grim, P. Pudil: Pattern Recognition by Probabilistic Neural Networks - Mixtures of Product Components versus Mixtures of Dependence Trees. NCTA2014 - International Conference on Neural Computation Theory and Applications, 65-75. SCITEPRESS, Rome 2014.   Download
4. Jiří Grim: Approximating Probability Densities by Mixtures of Gaussian Dependence Trees. Stochastic and Physical Monitoring Systems, SPMS 2014. ČVUT, Praha 2014.   Download
5. P. Pudil, L. Blažek, O. Částek, P. Somol, Jiří Grim: Identification of Corporate Competitiveness Factors – Comparing Different Approaches. Proceedings of the International Conference on Management, Leadership and Governance 2013, 259-267. Academic Conferences and Publishing International Limited, Reading 2013.   Download
6. Petr Somol, Jiří Grim, Jiří Filip, P. Pudil: On Stopping Rules in Dependency-Aware Feature Ranking. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 286-293. Springer, Heidelberg 2013.   Download
7. Jiří Filip, Jiří Grim, Michal Haindl: A Probabilistic Approach to Rough Texture Compression and Rendering. MUSCLE International Workshop on Computational Intelligence for Multimedia Understanding, 8-12. Bilkent University, Antalya, Turkey 2013.   Download
8. Jiří Grim, G. L. Lee: Evaluation of Screening Mammograms by Local Structural Mixture Models. Stochastic and Physical Monitoring Systems SPSM 2012, 51-61. Czech Technical University in Prague, Praha 2012.   Download
9. Petr Somol, Jiří Grim, P. Pudil: Fast Dependency-Aware Feature Selection in Very-High-Dimensional Pattern Recognition. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2011), 502-509. IEEE, Piscataway 2011.   Download
10. Jiří Grim: Preprocessing of Screening Mammograms Based on Local Statistical Models. Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL 2011, 1-5. ACM, Barcelona 2011.   Download
11. Michal Haindl, Vojtěch Havlíček, Jiří Grim: Colour Texture Representation Based on Multivariate Bernoulli Mixtures. 10th International Conference on Information Sciences, Signal Processing and their Applications, 578-581. IEEE, Los Alamitos 2010.   Download
12. Jiří Grim, Petr Somol, Pavel Pudil: Digital Image Forgery Detection by Local Statistical Models. 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 579-582. IEEE computer society, Los Alamitos, California 2010.   Download
13. Petr Somol, Jiří Grim, Pavel Pudil: The Problem of Fragile Feature Subset Preference in Feature Selection Methods and A Proposal of Algorithmic Workaround. Proc. 2010 Int. Conf. on Pattern Recognition, 4396-4399. IEEE Computer Society, Istanbul 2010.   Download
14. Jiří Grim, Jan Hora: Computational Properties of Probabilistic Neural Networks. Artificial Neural Networks – ICANN 2010, 31-40. Springer Verlag, Berlin Heidelberg 2010.   Download
15. * Petr Somol, Jiří Grim, Pavel Pudil: Criteria Ensembles in Feature Selection. Multiple Classifier Systems, LNCS 5519, 304-313. Springer, Berlin Heidelberg 2009.
16. * Jiří Grim, Jan Hora: Recognition of Properties by Probabilistic Neural Networks. Artificial Neural Networks - ICANN 2009, 165-174. Springer Verlag, Berlin, Heidelberg 2009.   Download
17. * Jiří Grim: Extraction of Binary Features by Probabilistic Neural Networks. Artificial Neural Networks - ICANN 2008, 52-61. Springer, Berlin 2008.
18. * Jiří Grim, Petr Somol, Pavel Pudil, I. Míková, M. Malec: Texture Oriented Image Inpainting based on Local Statistical Model. Proc. 10th IASTED Conf. on Signal & Image Processing, SIP 2008, 15-20. ACTA Press, Calgary 2008.   Download
19. * Petr Somol, Jana Novovičová, Jiří Grim, Pavel Pudil: Dynamic Oscillating Search Algorithm for Feature Selection. ICPR 2008 Proceedings (Int. Conf. on Pattern Recognition), 2308-2311. IEEE Computer Society, Tampa, Florida 2008.   Download
20. * Jiří Grim, Jana Novovičová, Petr Somol: Structural Poisson Mixtures for Classification of Documents. Proceedings of the 19th International Conference on Pattern Recognition, 1324-1327. IEEE Press, Los Alamitos 2008.   Download
21. * Jiří Grim, Jan Hora: Recurrent Bayesian Reasoning in Probabilistic Neural Networks. Artificial Neural Networks - ICANN 2007, 129-138. Springer, Berlin 2007.
22. * Jiří Grim, Michal Haindl, Petr Somol, Pavel Pudil: A subspace approach to texture modelling by using Gaussian mixtures. Proceedings of the 18th Conference on Pattern Recognition. ICPR 2006, 235-238. IEEE, Los Alamitos 2006.
23. * Michal Haindl, Jiří Grim, Pavel Pudil, M. Kudo: A hybrid BTF model based on Gaussian mixtures. Texture 2005. Proceedings of the 4th International Workshop on Texture Analysis, 95-100. IEEE, Los Alamitos 2005.
24. * Jiří Grim, Petr Somol, Michal Haindl, Pavel Pudil: A statistical approach to local evalution of a single texture image. Proceedings of the Sixtheenth Annual Symposium of the Pattern Recognition Association of South Africa, 171-176. University of Cape Town, Cape Town 2005.
25. * Michal Haindl, Jiří Grim, Petr Somol, Pavel Pudil, M. Kudo: A Gaussian mixture-based colour texture model. Proceedings of the 17th IAPR International Conference on Pattern Recognition, 177-180. IEEE, Los Alamitos 2004.
26. * Petr Somol, Pavel Pudil, Jiří Grim: On prediction mechanisms in Fast Branch & Bound algorithms. Structural, Syntactic, and Statistical Pattern Recognition. Joint IAPR International Workshops SSPR 2004 and SPR 2004. Proceedings, 716-724. Springer, Berlin 2004.   Download
27. * Jiří Grim, J. Hora, Pavel Boček, Petr Somol, P. Pudil: Information analysis of census data by using statistical models. Proceedings of the International Conference on Statistics - Investment in the Future, 1-7. Czech Statistical Office, Prague 2004.
28. * Jiří Grim, Petr Somol, Pavel Pudil, P. Just: Probabilistic neural network playing a simple game. Artificial Neural Networks in Pattern Recognition. Proceedings, 132-138. University of Florence, Florence 2003.
29. * Jiří Grim, Michal Haindl: A discrete mixtures colour texture model. Texture 2002. The 2nd International Workshop on Texture Analysis and Synthesis, 59-62. HeriotWatt University, Glasgow 2002.
30. * Jiří Grim, Pavel Pudil, Petr Somol: Boosting in probabilistic neural networks. Proceedings of the 16th International Conference on Pattern Recognition, 136-139. IEEE Computer Society, Los Alamitos 2002.   Download
31. * Jiří Grim, J. Kittler, Pavel Pudil, Petr Somol: Information analysis of multiple classifier fusion. Lecture Notes in Computer Science. 2096. Multiple Classifier Systems, 168-177. Springer, Berlin 2001.
32. * Jiří Grim, Pavel Boček, Pavel Pudil: Safe dissemination of census results by means of interactive probabilistic models. Proceedings of the ETK-NTTS 2001 Conference, 849-856. European Communities, Rome 2001.
33. * Petr Somol, Pavel Pudil, Jiří Grim: Branch & Bound algorithm with partial prediction for use with recursive and non-recursive criterion forms. Lecture Notes in Computer Science. 2013. Advances in Pattern Recognition - ICAPR 2001. Proceedings, 425-434. Springer, Heidelberg 2001.   Download
34. * Jiří Grim, Pavel Pudil, Petr Somol: Recognition of handwritten numerals by structural probabilistic neural networks. Proceedings of the Second ICSC Symposium on Neural Computation, 528-534. ICSC, Wetaskiwin 2000.   Download
35. * Jiří Grim, J. Kittler, Pavel Pudil, Petr Somol: Combining multiple classifiers in probabilistic neural networks. Lecture Notes in Computer Science. 1857. Multiple Classifier Systems, 157-166. Springer, Berlin 2000.
36. * Jiří Grim, Pavel Pudil, Petr Somol: Multivariate structural Bernoulli mixtures for recognition of handwritten numerals. Proceedings of the 15th International Conference on Pattern Recognition, 585-589. IEEE Computer Society, Los Alamitos 2000.
37. * Jiří Grim: Information approach to structural optimization of probabilistic neural networks. Fourth European Congress on Systems Science, 527-539. SESGE, Valencia 1999.
38. * Jiří Grim: A sequential modification of EM algorithm. Studies in Classification, Data Analysis, and Knowledge Organization. . Classification in the Information Age. Proceedings, 163-170. Springer, Berlin 1999.   Download
39. * Jiří Grim, Pavel Pudil: Interactive presentation of socio-economic databases by means of probabilistic models. Proceedings of the 17th International Conference on Mathematical Methods in Economics '99, 103-108. VŠE, Praha 1999.
40. * Jiří Grim: Discretization of probabilistic neural networks with bounded information loss. Preprints of the 3rd European IEEE Workshop on Computer-Intensive Methods in Control and Data Processing, 205-210. ÚTIA AV ČR, Praha 1998.   Download
41. * Jiří Grim, Jana Novovičová, Pavel Pudil, Petr Somol, F. J. Ferri: Initializing normal mixtures of densities. Proceedings of the 14th International Conference on Pattern Recognition, 886-890. IEEE, Los Alamitos 1998.
42. * Jiří Grim, Pavel Pudil: On virtually binary nature of probabilistic neural networks. Lecture Notes in Computer Science. 1451. Advances in Pattern Recognition. Proceedings, 765-774. Springer, Berlin 1998.
43. * Jiří Grim: Mixture of experts architectures for neural networks as a special case of conditional expectation formula. Proceedings of the 1st IAPR TC1 Workshop on Statistical Techniques in Pattern Recognition, 55-60. ÚTIA AV ČR, Praha 1997.   Download
44. * Igor Vajda, Jiří Grim: About the maximum information and maximum likelihood principles in neural networks. Proceedings of the 1st IAPR TC1 Workshop on Statistical Techniques in Pattern Recognition, 189-197. ÚTIA AV ČR, Praha 1997.
45. * Jiří Grim, Pavel Boček: Statistical model of Prague households for interactive presentation of census data. SoftStat '95. Advances in Statistical Software 5, 271-278. Lucius & Lucius, Stuttgart 1996.
46. * Jiří Grim: Design of multilayer neural networks by information preserving transforms. Third European Congress on Systems Science, 977-982. Edizioni Kappa, Roma 1996.   Download
47. * Jiří Grim: Maximum-likelihood design of layered neural networks. International Conference on Pattern Recognition. Proceedings, 85-89. IEEE Computer Society Press, Los Alamitos 1996.
48. * Jiří Grim: Individualized voting scheme - a democratic violation of the democratic voting principle. Cybernetics and Systems '94, 1081-1088. World Scientific, Singapore 1994.
49. * Jiří Grim: Multidimensional problems in the probabilistic expert system PES. Computer-Intensive Methods in Control and Signal Processing, 83-90. ÚTIA AV ČR, Praha 1994.
50. * Jiří Grim: A Dialog Presentation of Census Results by Means of the Probabilistic Expert System PES. 11th European Meeting on Cybernetics and Systems Research '92, 997-1004. World Scientific, Singapore 1992.
51. * Jiří Grim: Knowledge Representation and Uncertainty Processing in the Probabilistic Expert System PES. Workshop on Uncertainty Processing in Expert Systems, -. ÚTIA ČSAV, Prague 1991.
52. * Jiří Grim: Histogramový expertní systém. MEDSOFT 88 - seminář o medicínském software, 28-30. ČSVTS Fakulta všeobecného lékařství UK, Praha 1988.

Other publications (19)

1. Jiří Grim: Unsupervised Verification of Fake News by Public Opinion. Research Report 2390. UTIA, Praha 2021.   Download
2. Jiří Grim: Vyhodnocování grantové soutěže pomocí otevřené expertní databáze. Research Report 2371. ÚTIA AV ČR v.v.i, Praha 2018.   Download
3. Jiří Grim: Platební regulační mechanismus jako zdroj zvyšování platů ve zdravotnictví. Research Report 2373. ÚTIA AV ČR v.v.i, Praha 2018.   Download
4. Petr Somol, Jiří Grim: Fast Dependency-Aware Feature Selection in Very-High-Dimensional Pattern Recognition Problems. Research Report 2295. ÚTIA AV ČR, v.v.i, Praha 2011.   Download
5. * Jiří Grim, Petr Somol: Diagnostic Enhancement of Screening Mammograms by Means of Local Texture Models. Research Report 2217. ÚTIA AV ČR, Praha 2008.
6. * Jiří Grim: Neuromorphic Features of Probabilistic Neural Networks. Interní publikace - DAR - ÚTIA 2006/20. ÚTIA AV ČR, Praha 2006.
7. * Jiří Grim, Michal Haindl: A mixture-based colour texture model. Abstract. Recent Developments in Mixture Modelling. Abstracts, 58. Universität der Bundeswehr, Hamburg 2001.
8. * Jiří Grim: The models of conditional independence: Finite mixtures of product components and their application. Abstract. Recent Developments in Mixture Modelling. Abstracts, 59. Universität der Bundeswehr, Hamburg 2001.
9. * Jiří Grim: Latent Structure Analysis for Categorical Data. Research Report 2019. ÚTIA AV ČR, Praha 2001.
10. * Jiří Grim, Michal Haindl: A Monospectral Probabilistic Discrete Mixture Texture Model. Research Report 2018. ÚTIA AV ČR, Praha 2001.
11. * Jiří Grim: Pravděpodobnostní neuronové sítě. Research Report 1953. ÚTIA AV ČR, Praha 1999.
12. * Jiří Grim: Maximum-Likelihood Structuring of Probabilistic Neural Networks. Research Report 1894. ÚTIA AV ČR, Praha 1997.   Download
13. * Jiří Grim, J. Vejvalková: An Interative Inference Mechanism for the Probabilistic Expert System PES. Research Report 1866. ÚTIA AV ČR, Praha 1996.
14. * Jiří Grim, D. Vavruška: Probabilistic Knowledge-based Models Defined by Finite Distribution Mixtures. Research Report 1873. ÚTIA AV ČR, Praha 1996.
15. * Igor Vajda, Jiří Grim: On Information Theoretic Optimality of Radial Basis Function Neural Networks. Research Report 1864. ÚTIA AV ČR, Praha 1996.
16. * Jiří Grim: An Alternative Design of Mixture of Experts Architectures for Neural Networks. Research Report 1883. ÚTIA AV ČR, Praha 1996.
17. * Jiří Grim: Discretization in Probabilistic Neural Networks with Bounded Information Loss. Research Report 1878. ÚTIA AV ČR, Praha 1996.
18. * Igor Vajda, Jiří Grim: About Optimality of Probabilistic Basic Function Neural Networks. Research Report 1887. ÚTIA AV ČR, Praha 1996.
19. * Jiří Grim, Pavel Boček: Statistical Model of Prague Households for Interactive Presentation of Census Data. Research Report 1822. ÚTIA AV ČR, Praha 1994.

Miscellaneous (1)

1. * Pavel Pudil, Jana Novovičová, Jiří Grim: Proceedings of the 1st IAPR TC1 Workshop on Statistical Techniques in Pattern Recognition. ÚTIA AV ČR, Praha 1997.