Zacytuj

Bensusan, H., Giraud-Carrier, C. and Kennedy, C.J. (2000). A higher-order approach to meta-learning, in J. Cussens and A. Frisch (Eds.), Proceedings of the Work-in-Progress Track at the 10th International Conference on Inductive Logic Programming, Springer-Verlag, Berlin/Heidelberg, pp. 33-42.Search in Google Scholar

Brazdil, P., Giraud-Carrier, C., Soares, C. and Vilalta, R. (2009). Metalearning: Applications to Data Mining, Springer, Berlin/Heidelberg.10.1007/978-3-540-73263-1Search in Google Scholar

Brazdil, P., Soares, C. and da Costa, J.P. (2003). Ranking learning algorithms: Using IBL and meta-learning on accuracy and time results, Machine Learning 50(3): 251-277.10.1023/A:1021713901879Search in Google Scholar

Chan, P. and Stolfo, S.J. (1996). On the accuracy of metalearning for scalable data mining, Journal of Intelligent Information Systems 8(1): 5-28.10.1023/A:1008640732416Search in Google Scholar

Czarnowski, I. and J˛edrzejowicz, P. (2011). Application of agent-based simulated annealing and tabu search procedures to solving the data reduction problem, International Journal of Applied Mathematics and Computer Science 21(1): 57-68, DOI: 10.2478/v10006-011-0004-3.10.2478/v10006-011-0004-3Search in Google Scholar

Duch, W. and Grudzi´nski, K. (1999). Search and global minimization in similarity-based methods, International Joint Conference on Neural Networks, Washington, DC, USA, p. 742.Search in Google Scholar

Duch,W. and Itert, L. (2003). Committees of undemocratic competent models, Proceedings of the Joint International Conference on Artificial Neural Networks (ICANN) and the International Conference on Neural Information Processing (ICONIP), Istanbul, Turkey, pp. 33-36.Search in Google Scholar

Duch, W., Wieczorek, T., Biesiada, J. and Blachnik, M. (2004). Comparison of feature ranking methods based on information entropy, Proceedings of International Joint Conference on Neural Networks, Budapest, Hungary, pp. 1415-1420.Search in Google Scholar

Frank, A. and Asuncion, A. (2010). UCI machine learning repository, University of California, School of Information and Computer Science, Irvine, CA, http://archive.ics.uci.edu/ml.Search in Google Scholar

Gra˛bczewski, K. and Jankowski, N. (2011). Saving time and memory in computational intelligence system with machine unification and task spooling, Knowledge-Based Systems 24(5): 570-588.10.1016/j.knosys.2011.01.003Search in Google Scholar

Guyon, I. (2003). NIPS 2003 workshop on feature extraction, http://www.clopinet.com/isabelle/Projects/NIPS2003.Search in Google Scholar

Guyon, I. (2006). Performance prediction challenge, http://www.modelselect.inf.ethz.ch.Search in Google Scholar

Guyon, I., Gunn, S., Nikravesh, M. and Zadeh, L. (Eds.) (2006). Feature Extraction: Foundations and Applications, Springer, Berlin/Heidelberg.10.1007/978-3-540-35488-8Search in Google Scholar

Jankowski, N., Duch, W. and Gra˛bczewski, K. (Eds.) (2011). Meta-learning in Computational Intelligence, Studies in Computational Intelligence, Vol. 358, Springer, Berlin/Heidelberg.Search in Google Scholar

Jankowski, N. and Gra˛bczewski, K. (2005). Heterogenous committees with competence analysis, in N. Nedjah, L. Mourelle, M. Vellasco, A. Abraham and M. Köppen (Eds.), 5th International Conference on Hybrid Intelligent Systems, Rio de Janeiro, Brazil, IEEE Press, New York, NY, pp. 417-422.10.1109/ICHIS.2005.54Search in Google Scholar

Jankowski, N. and Gra˛bczewski, K. (2007). Handwritten digit recognition-Road to contest victory, IEEE Symposium Series on Computational Intelligence, IEEE Press, New York, NY, pp. 491-498.Search in Google Scholar

Jankowski, N. and Grochowski, M. (2004). Comparison of instances selection algorithms I: Algorithms survey, in L. Rutkowski, I. Siekmann, R. Tadeusiewicz and L.A. Zadeh (Eds.), Artificial Intelligence and Soft Computing, Lecture Notes in Artifical Intelligence, Vol. 3070, Springer-Verlag, Berlin/Heidelberg pp. 598-603.Search in Google Scholar

Jankowski, N. and Grochowski, M. (2005). Instances selection algorithms in the conjunction with LVQ, in M.H. Hamza (Ed.), Artificial Intelligence and Applications, ACTA Press, Innsbruck, pp. 453-459.Search in Google Scholar

Kadlec, P. and Gabrys, B. (2008). Learnt topology gating artificial neural networks, IEEE World Congress on Computational Intelligence, Hong Kong, China, pp. 2605-2612.Search in Google Scholar

Kohonen, T. (1986). Learning vector quantization for pattern recognition, Technical Report TKK-F-A601, Helsinki University of Technology, Espoo.Search in Google Scholar

Kordík, P. and ˇCerný, J. (2011). Self-organization of supervised models, in N. Jankowski, W. Duch and K. Gra˛bczewski (Eds.), Meta-learning in Computational Intelligence, Studies in Computational Intelligence, Vol. 358, Springer, Berlin/Heidelberg, pp. 179-223.10.1007/978-3-642-20980-2_6Search in Google Scholar

Korytkowski, M., Nowicki, R., Rutkowski, L. and Scherer, R. (2011). AdaBoost ensemble of DCOG rough-neuro-fuzzy systems, in P. J˛edrzejowicz, N.T. Nguyen and K. Hoang (Eds.), ICCCI (1), Lecture Notes in Computer Science, Vol. 6922, Springer, Berlin/Heidelberg, pp. 62-71.10.1007/978-3-642-23935-9_6Search in Google Scholar

Ł˛eski, J. (2003). A fuzzy if-then rule-based nonlinear classifier, International Journal of Applied Mathematics and Computer Science 13(2): 215-223.Search in Google Scholar

Peng, Y., Falch, P., Soares, C. and Brazdil, P. (2002). Improved dataset characterisation for meta-learning, 5th International Conference on Discovery Science, Luebeck, Germany, pp. 141-152.Search in Google Scholar

Pfahringer, B., Bensusan, H. and Giraud-Carrier, C. (2000). Meta-learning by landmarking various learning algorithms, International Conference on Machine Learning, Stanford, CA, USA, pp. 743-750.Search in Google Scholar

Prodromidis, A. and Chan, P. (2000). Meta-learning in distributed data mining systems: Issues and approaches, in H. Kargupta and P. Chan (Eds.), Book on Advances of Distributed Data Mining, AAAI Press, Menlo Park, CA.Search in Google Scholar

Scherer, R. (2010). Designing boosting ensemble of relational fuzzy systems, International Journal of Neural Systems 20(5): 381-388.10.1142/S012906571000252820945517Search in Google Scholar

Scherer, R. (2011). An ensemble of logical-type neuro-fuzzy systems, Expert Systems with Applications 38(10): 13115-13120.10.1016/j.eswa.2011.04.117Search in Google Scholar

Smith-Miles, K.A. (2008). Towards insightful algorithm selection for optimization using meta-learning concepts, IEEE World Congress on Computational Intelligence, Hong Kong, China, pp. 4117-4123.Search in Google Scholar

Todorovski, L. and Dzeroski, S. (2003). Combining classifiers with meta decision trees, Machine Learning Journal 50(3): 223-249.10.1023/A:1021709817809Search in Google Scholar

Tro´c, M. and Unold, O. (2010). Self-adaptation of parameters in a learning classifier system ensemble machine, International Journal of Applied Mathematics and Computer Science 20(1): 157-174, DOI: 10.2478/v10006-010-0012-8.10.2478/v10006-010-0012-8Search in Google Scholar

Witten, I.H. and Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, Amsterdam.Search in Google Scholar

eISSN:
2083-8492
Język:
Angielski
Częstotliwość wydawania:
4 razy w roku
Dziedziny czasopisma:
Mathematics, Applied Mathematics