{"id":316,"date":"2024-07-18T09:48:58","date_gmt":"2024-07-18T07:48:58","guid":{"rendered":"https:\/\/wi.pb.edu.pl\/p\/?page_id=316"},"modified":"2024-07-18T09:48:58","modified_gmt":"2024-07-18T07:48:58","slug":"the-list-of-publications","status":"publish","type":"page","link":"https:\/\/wi.pb.edu.pl\/p\/wojciech-kwedlo\/the-list-of-publications\/","title":{"rendered":"The List of Publications"},"content":{"rendered":"
    \n
  1. Kwedlo W., A parallel EM algorithm for Gaussian mixture models implemented on a NUMA system using OpenMP,<\/a> Proceedings of the 22nd Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP 2014), 2014, pp. 292-298.
    \n <\/li>\n
  2. Kwedlo W., A new nethod for random initialization of the EM algorithm for multivariate Gaussian mixture learning<\/a>, Proceedings of the 8th International Conference on Computer Recognition Systems (CORES 2013), 2013, pp. 81-90
    \n <\/li>\n
  3. Kwedlo W., A Clustering Method Combining Differential Evolution with the K<\/span>-means Algorithm<\/a>, Pattern Recognition Letters, 32(12), 2011, pp. 1613-1621.<\/li>\n
  4. Kwedlo W., Iwanowicz P., Using Genetic Algorithm for Selection of Initial Cluster Centers for the K-means method<\/a>,  Artificial Intelligence and Soft Computing 10th International Conference (ICAISC 2010), Lecture Notes in Artificial Intelligence 6114, 2010, pp. 165-172.<\/li>\n
  5. Bandurski K., Kwedlo W., A Lamarckian Hybrid of Differential Evolution and Conjugate Gradients for Neural Network Training<\/a>, Neural Processing Letters, 32(1),2010, pp. 31-44.<\/li>\n
  6. Kwedlo W., Bandurski K., A Parallel Differential Evolution Algorithm for Neural Network Training<\/a>, International Symposium on Parallel Computing in Electrical Engineering PARELEC 2006. <\/i>IEEE Computer Society Press, 2006, pp. 319-324.<\/li>\n
  7. Kwedlo W., Parallelizing Evolutionary Algorithms for Clustering Data<\/a>, PPAM 2005, Lecture Notes in Computer Science 3911, 2006, pp. 430-438<\/li>\n
  8. Wojciech Kwedlo, A Parallel Evolutionary Algorithm for Discovery of Decision Rules,Parallel Processing and Applied Mathematics, 5th International Conference, PPAM 2003, Lecture Notes in Computer Science 3019, Springer, 2004.\n

    Download pdf  (ppam2003.pdf 144 KB)<\/b><\/a> <\/li>\n

  9. (in Polish language) Wojciech Kwedlo,  Algorytmy ewolucyjne jako narz\u0119dzia pozyskiwania wiedzy z baz danych (Evolutionary Algorithms for Knowledge Discovery in Databases), Ph. D. thesis, Faculty of Computer Science, Bia\u0142ystok Technical University, 2003.
    \nDownload compressed postscript  (PhD-Kwedlo.zip 1.2 MB)<\/b><\/li>\n
  10. Kwedlo W., Kr\u0119towski M., Learning decision rules using a distributed evolutionary algorithm, TASK Quarterly<\/i> 6, 2002
    \nDownload compressed postscript of a draft version  (task2002.zip 218 KB)<\/b><\/li>\n
  11. Kwedlo W., Kr\u0119towski M., An evolutionary algorithm for cost-sensitive decision rule learning. Machine Learning: ECML 2001. 12th European Conference on Machine Learning<\/i>, Freiburg, Germany, September,3-7, 2001. Lecture Notes in Artificial Intelligence 2167<\/i>.
    \nDownload compressed postscript (ecml2001.ps.gz 120 KB)<\/b><\/li>\n
  12. Keller J., Bauer V., Kwedlo W., Application of data-mining and Knowledge discovery in automotive data engineering, Principles of data mining and knowledge discovery : 4th European Conference : PKDD’2000, <\/i>Lyon, France, September 13-16, 2000, Lecture Notes in Computer Science 1910<\/i><\/li>\n
  13. Kwedlo W., Kr\u0119towski M., Cost-sensitive induction of decision rules, Proceedings of IXth Workshop on Intelligent Information Systems, Bystra, Poland, June 12-16, 2000.
    \nDownload compressed postscript (iis2000.ps.gz 117 KB)<\/b><\/li>\n
  14. Kwedlo W., Kretowski M., An evolutionary algorithm using multivariate discretization for decision rule induction. Proceedings of 3rd<\/sup>European Conference on Principles and Practice of Knowledge Discovery in Databases. <\/i>Prague, Czech Republic, September 15-18, 1999. Springer Lecture Notes in Computer Science 1704.
    \nDownload compressed postscript (pkdd99.ps.gz 117 KB)<\/b>
    \n<\/b><\/li>\n
  15. Kwedlo W., Kretowski M., An evolutionary algorithm integrating discretization of continuous-valued attributes with learning decision rules. Proceedings of the 8th Workshop Intelligent Information Systems. <\/i>Ustron, Poland, June 14-18, 1999.
    \nDownload compressed postscript (iis99.ps.gz 76 KB)<\/b>
    \n<\/b><\/li>\n
  16. Kwedlo W., Kretowski M., Discovery of decision rules from databases: an evolutionary approach. Principles of Data Mining and Knowledge Discovery. Second European Symposium PKDD’98.<\/i> Nantes, France, September 23-26, 1998. Springer Lecture Notes in Computer Science 1510.
    \nDownload compressed postscript (pkdd98.ps.gz 73 KB)<\/b>
    \n<\/b><\/li>\n
  17. Kwedlo W., Kretowski M., Learning decision rules using an evolutionary algorithm and entropy-based discretization. Proceedings of the 7th Workshop Intelligent Information Systems. <\/i>Malbork, Poland, June 15-19, 1998.
    \nDownload compressed postscript (iis98.ps.gz 59 KB)<\/b>
    \n<\/b><\/li>\n
  18. Kwedlo W., An evolutionary algorithm for induction of classification rules and its application to the diagnosis of liver diseases, Proceedings of the 3rd Conference Neural Networks and Their Applications.<\/i> Kule, Poland, October 14-18, 1997.<\/li>\n
  19. Seredynski F., Kwedlo W, Kretowski M., An epsilon automata based multi-agent system for the dynamic mapping problem. Proceedings of the First International Workshop on Decentralised Intelligent and Multi-Agent Systems DIMAS-95 <\/i>. Cracow, Poland, 22-24 November, 1995.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

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