[1]   N. Jankowski and K. Grąbczewski. Increasing efficiency of data mining systems by machine unification and double machine cache. In Artificial Intelligence and Soft Computing, Lecture notes in computer science, pages 380–387. Springer, June 2010. (PDF).

[2]   K. Grąbczewski and N. Jankowski. Task management in advanced computational intelligence system. In Artificial Intelligence and Soft Computing, Lecture notes in computer science, pages 331–338. Springer, June 2010. (PDF).

[3]   N. Jankowski and K. Grąbczewski. Building meta-learning algorithms basing on search controlled by machine’s complexity and machines generators. In IEEE World Congress on Computational Intelligence, pages 3600–3607. IEEE Press, 2008. (PDF).

[4]   K. Grąbczewski and N. Jankowski. Meta-learning with machine generators and complexity controlled exploration. In Artificial Intelligence and Soft Computing, Lecture notes in computer science, pages 545–555. Springer, 2008. (PDF).

[5]   K. Grąbczewski and N. Jankowski. Control of complex machines for meta-learning in computational intelligence. In Computational Intelligence, Man-Machine Systems and Cybernetics, pages 287–293. WSEAS, 2007. (PDF).

[6]   K. Grąbczewski and N. Jankowski. Meta-learning as scheme-based search with complexity control. In International Joint Conference on Neural Network. Workshop on Meta-Learning, pages 3–8, USA, 2007. IEEE Press. (PDF).

[7]   N. Jankowski and K. Grąbczewski. Gained knowledge exchange and analysis for meta-learning. In Proceedings of International Conference on Machine Learning and Cybernetics, pages 795–802, Hong Kong, China, 2007. IEEE Press. (PDF).

[8]   N. Jankowski and K. Grąbczewski. Learning machines information distribution system with example applications. In Computer Recognition systems 2, Advances in Soft Computing, pages 205–215. Springer, 2007. (PDF).

[9]   K. Grąbczewski and N. Jankowski. Meta-learning architecture for knowledge representation and management in computational intelligence. International Journal of Information Technology and Intelligent Computing, 2(2):27, 2007. (PDF).

[10]   N. Jankowski and K. Grąbczewski. Handwritten digit recognition — road to contest victory. In IEEE Symposium Series on Computational Intelligence, pages 491–498, USA, 2007. IEEE Press. (PDF).

[11]   K. Grąbczewski and N. Jankowski. Toward versatile and efficient meta-learning: Knowledge representation and management in computational intelligence. In IEEE Symposium Series on Computational Intelligence, pages 51–58, USA, 2007. IEEE Press. (PDF).

[12]   N. Jankowski and K. Grąbczewski. Learning machines. In Isabelle Guyon, Steve Gunn, Masoud Nikravesh, and Lofti Zadeh, editors, Feature extraction, foundations and Applications, Studies in fuzziness and soft computing, pages 29–64. Springer, 2006. (PDF).

[13]   K. Grąbczewski and N. Jankowski. Mining for complex models comprising feature selection and classification. In Isabelle Guyon, Steve Gunn, Masoud Nikravesh, and Lofti Zadeh, editors, Feature extraction, foundations and Applications, Studies in fuzziness and soft computing, pages 473–489. Springer, 2006. (PDF).

[14]   N. Jankowski and K. Grąbczewski. Heterogenous committees with competence analysis. In N. Nedjah, L.M. Mourelle, M.M.B.R Vellasco, A. Abraham, and M. Köppen, editors, Fifth International conference on Hybrid Intelligent Systems, pages 417–422, Brasil, Rio de Janeiro, November 2005. IEEE, Computer Society. (PDF).

[15]   K. Grąbczewski and N. Jankowski. Feature selection with decision tree criterion. In N. Nedjah, L.M. Mourelle, M.M.B.R Vellasco, A. Abraham, and M. Köppen, editors, Fifth International conference on Hybrid Intelligent Systems, pages 212–217, Brasil, Rio de Janeiro, November 2005. IEEE, Computer Society. (PDF).

[16]   W. Duch, N. Jankowski, and K. Grąbczewski. Computational intelligence methods for information understanding and information management. In The 4th International Conference on Information and Management Sciences (IMS2005), pages 281–287, Kunming, China, 2005. California Polytechnic State University. (PDF).

[17]   N. Jankowski and M. Grochowski. Instances selection algorithms in the conjunction with LVQ. In M. H. Hamza, editor, Artificial Intelligence and Applications, pages 453–209, Innsbruck, Austria, February 2005. ACTA Press. (PDF).

[18]   M. Grochowski and N. Jankowski. Comparison of instances selection algorithms: I. results and comments. In Artificial Intelligence and Soft Computing, Lecture notes in computer science, pages 580–585. Springer, June 2004. (PDF).

[19]   N. Jankowski and M. Grochowski. Comparison of instances selection algorithms: I. Algorithms survey. In Artificial Intelligence and Soft Computing, Lecture notes in computer science, pages 598–603. Springer, June 2004. (PDF).

[20]   N. Jankowski, K. Grąbczewski, and W. Duch. GhostMiner 3.0. FQS Poland, Fujitsu, Kraków, Poland, 2004.

[21]   N. Jankowski, K. Grąbczewski, and W. Duch. GhostMiner 2.0. FQS Poland, Fujitsu, Kraków, Poland, 2003.

[22]   N. Jankowski. Ontogeniczne sieci neuronowe. O sieciach zmieniających swoją strukturę. Akademicka Oficyna Wydawnicza, Warszawa, 2003. (PDF).

[23]   N. Jankowski and K. Grąbczewki. Toward optimal SVM. In The Third IASTED International Conference on Artificial Intelligence and Applications, pages 451–456, Anaheim, Calgary, Zurich, September 2003. The International Association of Science and Technology for Development, ACTA Press. (PDF).

[24]   N. Jankowski. Discrete feature weighting & selection algorithm. In 2003 International Joint Conference on Neural Networks, pages 636–641, Portland, USA, July 2003. The IEEE Neural Networks Society. (PDF).

[25]   K. Grąbczewki and N. Jankowski. Symbolic data transformations for continuous data oriented models. In International Conference on Artificial Neural Networks, pages 359–366, Turky, 2003. (PDF).

[26]   N. Jankowski. Discrete quasi-gradient features weighting algorithm. In L. Rutkowski and J. Kacprzyk, editors, Neural Networks and Soft Computing. Proceedings of the 6th International Conference on Neural Networks and Soft Computing (ICNNSC), Advances in Soft Computing, pages 194–199. Springer-Verlag, Zakopane, Poland, June 2002. (PDF).

[27]   N. Jankowski and K. Grąbczewski. From LATEX to HTML Help. In Proceedings of the XIII European TEX Conference, pages 102–105, Bachotek, Poland, may 2002. (PDF).

[28]   N. Jankowski and W. Duch. Optimal transfer function neural networks. In 9th European Symposium on Artificial Neural Networks, pages 101–106, Bruges, Belgium, April 2001. (PDF).

[29]   W. Duch and N. Jankowski. Transfer functions: hidden possibilities for better neural networks. In 9th European Symposium on Artificial Neural Networks, pages 81–94, Bruges, Belgium, April 2001. (PDF).

[30]   W. Duch, R. Adamczak, K. Grąbczewski, and N. Jankowski. Neural methods of knowledge extraction. Control and Cybernetics, 29(4):997–1018, 2000. (PDF).

[31]    W. Duch, R. Adamczak, K. Grąbczewski, K. Grudzinski, N. Jankowski, and A. Naud. Understanding the data: extraction, optimization and interpretation of logical rules. In 7th International Conference on Neural Information, page 53, Dae-jong, Korea, November 2000.

[32]   N. Jankowski and Jerzy Gomuła. Simultaneous differential diagnoses basing on MMPI inventory using neural networks and decision trees methods. In L. Bobrowski, J. Doroszewski, E. Marubini, and N. Victor, editors, Statistics and Clinical Practice, pages 89–95, Warsaw, Poland, June 2000. (PDF).

[33]   N. Jankowski. Data regularization. In L. Rutkowski and R. Tadeusiewicz, editors, Neural Networks and Soft Computing, pages 209–214, Zakopane, Poland, June 2000. (PDF).

[34]   N. Jankowski. Probabilistic intervals of confidence. In L. Rutkowski and R. Tadeusiewicz, editors, Neural Networks and Soft Computing, pages 215–220, Zakopane, Poland, June 2000. (PDF).

[35]   W. Duch and N. Jankowski. Taxonomy of neural transfer functions. In Shun-Ichi Amari, C. Lee Giles, Marco Gori, and Vincenzo Piuri, editors, International Join Conference on Neural Networks, volume III, pages 477–484, Como, Italy & Los Alamitos, California, July 2000. Computer Society and IEEE. (PDF).

[36]   W. Duch, N. Jankowski, K. Grąbczewski, and Rafał Adamczak. Optimization and interpretation of rule-based classifiers. In Intelligent Information Systems, Advances in Soft Computing, pages 1–14, Bystra, Poland, June 2000. Springer-Verlag. (PDF).

[37]   W. Duch, R. Adamczak, K. Grąbczewski, K. Grudzinski, N. Jankowski, and A. Naud. Extraction of knowledge from data using computational intelligence methods. In 7th International Conference on Neural Information, page 53, Dae-jong, Korea, November 2000.

[38]   N. Jankowski and W. Duch. Ontogeniczne sieci neuronowe. In Włodzisław Duch, Józef Korbicz, Leszek Rutkowski, and Ryszard Tadeusiewicz, editors, Sieci Neuronowe, Biocybernetyka i inżynieria biomedyczna, pages 257–294. Akademicka Oficyna Wydawnicza EXIT, Warszawa, 2000. (PDF).

[39]   N. Jankowski. Ontogenic neural networks and their applications to classification of medical data. PhD thesis, Department of Computer Methods, Nicholas Copernicus University, Toruń, Poland, 1999. (PDF).

[40]   N. Jankowski. Neural turing machine. Summer School Conference on Connectionist Modelling Oxford (slides), July 1999, (PDF).

[41]   W. Duch and N. Jankowski. Survey of neural transfer functions. Neural Computing Surveys, 2:163–212, 1999. (PDF).

[42]   N. Jankowski. Approximation and classification in medicine with IncNet neural networks. In Machine Learning and Applications. Workshop on Machine Learning in Medical Applications, pages 53–58, Chania, Greece, July 1999. Hellenic Artificial Intelligence Society. (PDF).

[43]   N. Jankowski. Flexible transfer functions with ontogenic neural. Technical report, Computational Intelligence Lab, DCM NCU, Toruń, Poland, 1999. (PDF).

[44]   N. Jankowski. Approximation with RBF-type neural networks using flexible local and semi-local transfer functions. In 4th Conference on Neural Networks and Their Applications, pages 77–82, Zakopane, Poland, May 1999. Polish Neural Networks Society. (PDF).

[45]   N. Jankowski. Controlling the structure of neural networks that grow and shrink. In Second International Conference on Cognitive and Neural Systems, Boston, USA, May 1998. (PDF).

[46]   N. Jankowski and V. Kadirkamanathan. Statistical control of RBF-like networks for classification. In 7th International Conference on Artificial Neural Networks, pages 385–390, Lausanne, Switzerland, October 1997. Springer-Verlag. (PDF).

[47]   N. Jankowski and V. Kadirkamanathan. Statistical control of growing and pruning in RBF-like neural networks. In Third Conference on Neural Networks and Their Applications, pages 663–670, Kule, Poland, October 1997. Polish Neural Networks Society. (PDF).

[48]   R. Adamczak, W. Duch, and N. Jankowski. New developments in the feature space mapping model. In Third Conference on Neural Networks and Their Applications, pages 65–70, Kule, Poland, October 1997. Polish Neural Networks Society. (PDF).

[49]   W. Duch, R. Adamczak, and N. Jankowski. Initialization of adaptive parameters in density networks. In Third Conference on Neural Networks and Their Applications, pages 99–104, Kule, Poland, October 1997. (PDF).

[50]   W. Duch, R. Adamczak, and N. Jankowski. Initialization and optimization of multilayered perceptrons. In Third Conference on Neural Networks and Their Applications, pages 105–110, Kule, Poland, October 1997. (PDF).

[51]   W. Duch, R. Adamczak, and N. Jankowski. New developments in the feature space mapping model. Technical Report CIL-KMK-2/97, Computational Intelligence Lab, DCM NCU, Toruń, Poland, October 1997. (long version).

[52]   W. Duch and N. Jankowski. New neural transfer functions. Journal of Applied Mathematics and Computer Science, 7(3):639–658, 1997. (PDF).

[53]   W. Duch, Adamczak Rafał, N. Jankowski, Antoine Naud, Jerzy Gomuła, and Tomasz Kucharski. Neural-based classification and visualization methods with applications to psychometry. In 34th International Seminar on Statistics and Clinical Practice, Warszawa, 1996.

[54]   W. Duch, R. Adamczak, and N. Jankowski. Improved memory-based classification. In A. B. Bulsari, S. Kallio, and D. Tsaptsinos, editors, Proceedings of the International Conference EANN ’96, pages 447–450, June 1996. (PDF).

[55]   W. Duch and N. Jankowski. Bi-radial transfer functions. In Second Conference on Neural Networks and Their Applications, pages 131–137, Szczyrk, Poland, May 1996. (PDF).

[56]   W. Duch and N. Jankowski. Bi-radial transfer functions. Technical Report UMK-KMK-TR 1/96, Department of Computer Methods, Nicholas Copernicus University, Toruń, Poland, 1995.

[57]   N. Jankowski. Matlab — plusy kontra minusy. Technical report, Department of Computer Methods, Nicholas Copernicus University in Torun, Poland, 1995. (PDF).

[58]   W. Duch, N. Jankowski, A. Naud, and R. Adamczak. Feature space mapping: a neurofuzzy network for system identification. In Proceedings of the European Symposium on Artificial Neural Networks, pages 221–224, Helsinki, August 1995. (Postscript).

[59]   N. Jankowski. Applications of Levin’s universal optimal search algorithm. In E. Kącki, editor, System Modeling Control’95, volume 3, pages 34–40, Łódź, Poland, May 1995. Polish Society of Medical Informatics. (PDF).

[60]   W. Duch and N. Jankowski. Complex systems, information theory and neural networks. In Proceedings of the first national conference: Neural Network And Their Applications, volume 1, pages 224–231, Kule, Poland, April 1994. Institute of electronics and control system, Technical University of Częstochowa. (PDF).