Abstract
Number of training patterns has a huge impact on artificial neural networks training process, not only because of time-consuming aspects but also on network capacities. During training process the error for the most patterns reaches low error very fast and is hold to the end of training so can be safely removed without prejudice to further training process. Skilful removal of patterns during training allow to achieve better training results decreasing both training time and training error. The paper presents some implementations of this approach for Error Correction algorithm and RBF networks. The effectiveness of proposed methods has been confirmed by several experiments.
This work was supported by the National Science Centre, Cracow, Poland under Grant No. 2013/11/B/ST6/01337.
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References
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533–6 (1986)
Fahlman, S.E., Lebiere, C.: The cascade-correlation learning architecture. In: Advances in Neural Information Processing Systems 2. pp. 524–532. Morgan Kaufmann, San Mateo (1990)
Wilamowski, B.M., Bo, W., Korniak, J.: Big data and deep learning. In: 20th Jubilee IEEE International Conference on Intelligent Engineering Systems (INES 2016), 30 June–July 2, pp. 11–16 (2016)
Lang, K.L., Witbrock, M.J.: Learning to tell two spirals apart. In: Proceedings of the 1988 Connectionists Models Summer School. Morgan Kaufman (1998)
Wilamowski, B.M., Korniak, J.: Learning architectures with enhanced capabilities and easier training. In: 19th IEEE International Conference on Intelligent Engineering Systems (INES 2015), 03–05 September, pp. 21–29 (2015)
Bengio, Y.: Learning deep architectures for AI. Found. Trends in Mach. Learn. 2(1), 1127 (2009)
Ciresan, D.C., Meier, U., Gambardella, L.M., Schmidhuber, J.: Deep big simple neural nets excel on handwritten digit recognition. CoRR (2010)
Wilamowski, B.M., Yu, H.: Neural network learning without backpropagation. IEEE Trans. Neural Netw. 21(11), 1793–80 (2010)
Werbo, P.J.: Back-propagation: past and future. In: Proceeding of International Conference on Neural Networks, San Diego, CA, vol. 1, pp. 343–354 (1988)
Yu, H., Reiner, P., Xie, T., Bartczak, T., Wilamowski, B.M.: An incremental design of radial basis function networks. IEEE Trans. Neural Netw. Learn. Syst. 25(10), 1793–80 (2014)
Nguyen, G.H., Bouzerdoum, A., Phung, S.L.: Efficient supervised learning with reduced training exemplars. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), Hong Kong, pp. 2981–2987 (2008)
Lozano, M.T.: Data reduction techniques in classification processes. Ph.D. Dissertation, Universitat Jaume I, Spain (2007)
Chouvatut, V., Jindaluang, W., Boonchieng, E.: Training set size reduction in large dataset problems. In: 2015 International Computer Science and Engineering Conference (ICSEC), Chiang Mai, pp. 1–5 (2015)
Kolbusz, J., Rozycki, P.: Outliers elimination for error correction algorithm improvement. In: CS&P Proceedings 24th International Workshop Concurrency, Specification & Programming, (CS & P 2015), vol. 2, pp. 120–129 (2015)
Xie, T.: Growing and learning algorithms of radial basis function networks. Ph.D. Dissertation, Auburn University, USA (2013)
Dieterich, J., Hartke, B.: Empirical review of standard benchmark functions using evolutionary global optimization. Appl. Math. 3(10A), 1552–64 (2012)
Wilamowski, B.M., Jaeger, R.C.: Implementation of RBF type networks by MLP networks. In: IEEE International Conference on Neural Networks (ICNN 96), pp. 1670–1675 (1996)
Wu, X., Wilamowski, B.M.: Advantage analysis of sigmoid based RBF networks. In: Proceedings of the 17th IEEE International Conference on Intelligent Engineering Systems (INES 13), pp. 243–248 (2013)
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Rozycki, P., Kolbusz, J., Lysenko, O., Wilamowski, B.M. (2017). Improvement of RBF Training by Removing of Selected Pattern. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2017. Lecture Notes in Computer Science(), vol 10245. Springer, Cham. https://doi.org/10.1007/978-3-319-59063-9_14
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