Publications - Jiří Grim


Books and chapters (3)

1. Igor Vajda, Jiří Grim: Neural networks. Systems Science and Cybernetics, 224-248. Eolss Publishers-UNESCO, Oxford 2008.   Download
2. Jiří Grim: Pravděpodobnostní neuronové sítě. Umělá inteligence (4), 276-312. Academia, Praha 2003.
3. 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 (31)

1. 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
2. Jiří Grim: Sequential pattern recognition by maximum conditional informativity. Pattern Recognition Letters 45:1 (2014), 39-45. Elsevier.   Download
3. 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
4. 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
5. 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
6. Jiří Grim, J. Hora, Petr Somol, Pavel Boček, P. Pudil: Interaktivní statistický model dat ze sčítání lidu v České republice v r. 2001. Statistika 89:4 (2009), 285-299.   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.
8. Michal Haindl, Vojtěch Havlíček, Jiří Grim: Probabilistic Discrete Mixtures Colour Texture Models. Lecture Notes in Computer Science 5197 (2008), 675-682.   Download
9. Jiří Grim, Jan Hora: Iterative principles of recognition in probabilistic neural networks. Neural Networks 21:6 (2008), 838-846. Elsevier.
10. Jiří Grim, Jan Hora: Minimum Information Loss Cluster Analysis for Cathegorical Data. Lecture Notes in Computer Science 2007, 233-247.
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: EM cluster analysis for categorical data. Lecture Notes in Computer Science 44:4109 (2006), 640-648.
14. 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
15. 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, Michal Haindl: Texture modelling by discrete distribution mixtures. Computational Statistics and Data Analysis 41, 603-615. Elsevier.
18. 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..
19. Jiří Grim, J. Kittler, Pavel Pudil, Petr Somol: Multiple classifier fusion in probabilistic neural networks. Pattern Analysis and Applications 5:7 (2002), 221-233.
20. Jiří Grim, Pavel Pudil, Petr Somol: Probabilistic information retrieval from census data based on distribution mixtures. Acta Oeconomica Pragensia 8:2 (2000), 41-47.
21. Jiří Grim: Self-organizing maps and probabilistic neural networks. Neural Network World 10:3 (2000), 407-415. Ústav informatiky AV ČR, v. v. i..
22. Jiří Grim, J. Vejvalková: An iterative inference mechanism for the probabilistic expert system PES. International Journal of General Systems 27, 373-396.
23. 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..
24. 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..
25. * Jiří Grim: Knowledge representation and uncertainty processing in the probabilistic expert system PES. International Journal of General Systems 22:2 (1994), 103-111.
26. * Jiří Grim, Pavel Kolář: Akciové společnosti s téměř jistým převisem poptávky ve 4. kole. Hospodářské noviny 209 (1992), 8.
27. * Jiří Grim, Pavel Kolář: Kuponová privatizace: odhadování rizika převisů. Akcionář 3:21 (1992), 10-11.
28. * Pavel Boček, Jiří Grim, Pavel Kolář: Kupónová privatizace zdárně pokračuje. Akcionář 3:15 (1992), 8.
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: Kam s kupóny v druhém kole?. Hospodářské noviny 135 (1992), 8.
31. * 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 2015.   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. 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
6. 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
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. 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
12. 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
13. 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
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. 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
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. 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
20. Jiří Grim: Extraction of Binary Features by Probabilistic Neural Networks. Artificial Neural Networks - ICANN 2008, 52-61. Springer, Berlin 2008.
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. 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.
24. 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.
25. 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
26. 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.
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, 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
30. 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.
31. 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
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. 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.
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, 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.
36. 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.
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, 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.
39. 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
40. 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.
41. 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.
42. 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
43. 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.
44. 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
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: Multidimensional problems in the probabilistic expert system PES. Computer-Intensive Methods in Control and Signal Processing, 83-90. ÚTIA AV ČR, Praha 1994.
49. * Jiří Grim: Individualized voting scheme - a democratic violation of the democratic voting principle. Cybernetics and Systems '94, 1081-1088. World Scientific, Singapore 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 (16)

1. 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
2. Jiří Grim, Petr Somol: Diagnostic Enhancement of Screening Mammograms by Means of Local Texture Models. Research Report 2217. ÚTIA AV ČR, Praha 2008.
3. Jiří Grim: Neuromorphic Features of Probabilistic Neural Networks. Interní publikace - DAR - ÚTIA 2006/20. ÚTIA AV ČR, Praha 2006.
4. 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.
5. Jiří Grim: Latent Structure Analysis for Categorical Data. Research Report 2019. ÚTIA AV ČR, Praha 2001.
6. Jiří Grim, Michal Haindl: A mixture-based colour texture model. Abstract. Recent Developments in Mixture Modelling. Abstracts, 58. Universität der Bundeswehr, Hamburg 2001.
7. Jiří Grim, Michal Haindl: A Monospectral Probabilistic Discrete Mixture Texture Model. Research Report 2018. ÚTIA AV ČR, Praha 2001.
8. Jiří Grim: Pravděpodobnostní neuronové sítě. Research Report 1953. ÚTIA AV ČR, Praha 1999.
9. Jiří Grim: Maximum-Likelihood Structuring of Probabilistic Neural Networks. Research Report 1894. ÚTIA AV ČR, Praha 1997.   Download
10. Igor Vajda, Jiří Grim: About Optimality of Probabilistic Basic Function Neural Networks. Research Report 1887. ÚTIA AV ČR, Praha 1996.
11. Igor Vajda, Jiří Grim: On Information Theoretic Optimality of Radial Basis Function Neural Networks. Research Report 1864. ÚTIA AV ČR, Praha 1996.
12. Jiří Grim, D. Vavruška: Probabilistic Knowledge-based Models Defined by Finite Distribution Mixtures. Research Report 1873. ÚTIA AV ČR, Praha 1996.
13. Jiří Grim: Discretization in Probabilistic Neural Networks with Bounded Information Loss. Research Report 1878. ÚTIA AV ČR, Praha 1996.
14. Jiří Grim, J. Vejvalková: An Interative Inference Mechanism for the Probabilistic Expert System PES. Research Report 1866. ÚTIA AV ČR, Praha 1996.
15. Jiří Grim: An Alternative Design of Mixture of Experts Architectures for Neural Networks. Research Report 1883. ÚTIA AV ČR, Praha 1996.
16. * 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.