Training neural networks on noisy data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8467 LNAI, Issue PART 1, 2014, Pages 131-142

  Rusiecki, Andrzej ^a   Kordos, Mirosław ^b   Kamiński, Tomasz ^b   Greń, Krzysztof ^b  

^a WROCLAW UNIVERSITY OF TECHNOLOGY   (Poland)

^b UNIVERSITY OF BIELSKO BIALA   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

SOFT COMPUTING; STATISTICS;

INSTANCE SELECTION; MLP NEURAL NETWORKS; NOISY DATA; NON-DIFFERENTIABLE; OBJECTIVE FUNCTIONS; OUTLIER DETECTION; REGRESSION PROBLEM; SECOND GROUP;

ARTIFICIAL INTELLIGENCE;

EID: 84902578412     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-07173-2_13     Document Type: Conference Paper

References (32)

1. Beliakov, G., Kelarev, A., Yearwood, J.: Derivative-free optimization and neural networks for robust regression. Optimization 61(12), 1467-1490 (2012)
2. Ben-Gal, I.: Outlier detection. Kluwer Academic Publishers (2005)
3. Chen, D., Jain, R.: A robust backpropagation learning algorithm for function approximation. IEEE Transactions on Neural Networks 5(3), 467-479 (1994)
4. Chuang, C.C., Su, S.F., Hsiao, C.C.: The annealing robust backpropagation (arbp) learning algorithm. IEEE Transactions on Neural Networks 11(5), 1067-1077 (2000)
5. El-Melegy, M.T., Essai, M.H., Ali, A.A.: Robust training of artificial feedforward neural networks. In: Hassanien, A.-E., Abraham, A., Vasilakos, A.V., Pedrycz, W. (eds.) Foundations of Computational, Intelligence Volume 1. SCI, vol. 201, pp. 217-242. Springer, Heidelberg (2009)
6. El-Melegy, M.: Random sampler m-estimator algorithm for robust function approximation via feed-forward neural networks. In: The 2011 International Joint Conference on Neural Networks (IJCNN), pp. 3134-3140 (2011)
7. El-Melegy, M.: Ransac algorithm with sequential probability ratio test for robust training of feed-forward neural networks. In: The 2011 International Joint Conference on Neural Networks (IJCNN), pp. 3256-3263 (2011)
8. El-Melegy, M.: Random sampler m-estimator algorithm with sequential probability ratio test for robust function approximation via feed-forward neural networks. IEEE Transactions on Neural Networks and Learning Systems 24(7), 1074-1085 (2013)
9. Golak, S., Burchart-Korol, D., Czaplicka-Kolarz, K., Wieczorek, T.: Application of neural network for the prediction of eco-efficiency. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds.) ISNN 2011, Part III. LNCS, vol. 6677, pp. 380-387. Springer, Heidelberg (2011)
10. Guillen, A.: Applying mutual information for prototype or instance selection in regression problems. In: ESANN 2009 (2009)
11. Hampel, F.R., Ronchetti, E.M., Rousseeuw, P.J., Stahel, W.A.: Robust Statistics: The Approach Based on Influence Functions (Wiley Series in Probability and Statistics), revised edn. Wiley-Interscience, New York (2005)
12. Hart, P.: The condensed nearest neighbor rule (corresp.). IEEE Transactions on Information Theory 14(3), 515-516 (1968)
13. Huber, P.J.: Robust Statistics. Wiley Series in Probability and Statistics. Wiley-Interscience (1981)
14. Kordos, M., Duch, W.: Variable Step Search Algorithm for Feedforward Networks. Neurocomputing 71(13-15), 2470-2480 (2008)
15. Kordos, M., Białka, S., Blachnik, M.: Instance selection in logical rule extraction for regression problems. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part II. LNCS, vol. 7895, pp. 167-175. Springer, Heidelberg (2013)
16. Kordos, M., Blachnik, M., Strzempa, D.: Do We Need Whatever More Than k-NN? In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010, Part I. LNCS (LNAI), vol. 6113, pp. 414-421. Springer, Heidelberg (2010)
17. Kordos, M., Rusiecki, A.: Improving MLP Neural Network Performance by Noise Reduction. In: Dediu, A.-H., Martín-Vide, C., Truthe, B., Vega-Rodríguez, M.A. (eds.) TPNC 2013. LNCS, vol. 8273, pp. 133-144. Springer, Heidelberg (2013)
18. Liano, K.: Robust error measure for supervised neural network learning with outliers. IEEE Transactions on Neural Networks 7(1), 246-250 (1996)
19. Pernia-Espinoza, A.V., Ordieres-Mere, J.B., de Pison, F.J.M., Gonzalez-Marcos, A.: Tao-robust backpropagation learning algorithm. Neural Networks 18(2), 191-204 (2005)
20. Prechelt, L.: Proben1 - a set of neural network benchmark problems and benchmarking rules. Tech. rep. (1994)
21. Rousseeuw, P.J., Leroy, A.M.: Robust Regression and Outlier Detection. JohnWiley & Sons, Inc., New York (1987)
22. Rousseeuw, P.J.: Least median of squares regression. Journal of the American Statistical Association 79(388), 871-880 (1984)
23. Rusiecki, A.: Robust LTS backpropagation learning algorithm. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds.) IWANN 2007. LNCS, vol. 4507, pp. 102-109. Springer, Heidelberg (2007)
24. Rusiecki, A.: Robust MCD-based backpropagation learning algorithm. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 154-163. Springer, Heidelberg (2008)
25. Rusiecki, A.: Robust learning algorithm based on iterative least median of squares. Neural Processing Letters 36(2), 145-160 (2012)
26. Rusiecki, A.: Robust learning algorithm based on LTA estimator. Neurocomputing 120, 624- 632 (2013)
27. Salvador, G., Derrac, J., Ramon, C.: Prototype selection for nearest neighbor classification: Taxonomy and empirical study. IEEE Transactions on Pattern Analysis and Machine Intelligence 34, 417-435 (2012)
28. Tolvi, J.: Genetic algorithms for outlier detection and variable selection in linear regression models. Soft Computing 8, 527-533 (2004)
29. Merz, C., Murphy, P.: Uci repository of machine learning databases (2013), http://www.ics.uci.edu/mlearn/MLRepository.html
30. Wilson, D.L.: Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on Systems, Man and Cybernetics SMC-2(3), 408-421 (1972)
31. Zhang, J.: Intelligent selection of instances for prediction functions in lazy learning algorithms. Artifcial Intelligence Review 11, 175-191 (1997)
32. Source code and datasets used in the paper, https://code.google.com/p/ mlp2013/