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A Fast Neural Network Learning Algorithm with Approximate Singular Value Decomposition

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International Journal of Applied Mathematics and Computer Science
Information Technology for Systems Research (special section, pp. 427-515), Piotr Kulczycki, Janusz Kacprzyk, László T. Kóczy, Radko Mesiar (Eds.)

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Bishop, C.M. (1991). Training with noise is equivalent to Tikhonov regularization, Neural Computation7(1): 108–116.10.1162/neco.1995.7.1.108Search in Google Scholar

Boser, B.E., Guyon, I.M. and Vapnik, V. (1992). A training algorithm for optimal margin classifiers, in D. Haussler (Ed.), Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, Pittsburgh, PA, USA, pp. 144–152.10.1145/130385.130401Search in Google Scholar

Broomhead, D.S. and Lowe, D. (1988). Multivariable functional interpolation and adaptive networks, Complex Systems2(3): 321–355.Search in Google Scholar

Dumais, S.T. (2005). Latent semantic analysis, Annual Review of Information Science and Technology38(1): 188–230.10.1002/aris.1440380105Search in Google Scholar

Eirola, E., Lendasse, A., Vandewalle, V. and Biernacki, C. (2014). Mixture of Gaussians for distance estimation with missing data, Neurocomputing131: 32–42.10.1016/j.neucom.2013.07.050Search in Google Scholar

Goodfellow, I., Bengio, Y. and Courville, A. (2016). Deep Learning, MIT Press, Cambridge, MA, http://www.deeplearningbook.org.Search in Google Scholar

Górecki, T. and Łuczak, M. (2013). Linear discriminant analysis with a generalization of the Moore–Penrose pseudoinverse, International Journal of Applied Mathematics and Computer Science23(2): 463–471, DOI: 10.2478/amcs-2013-0035.10.2478/amcs-2013-0035Open DOISearch in Google Scholar

Halko, N., Martinsson, P.G. and Tropp, J.A. (2011). Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions, SIAM Review53(2): 217–288.10.1137/090771806Search in Google Scholar

Heseltine, T., Pears, N., Austin, J. and Chen, Z. (2003). Face recognition: A comparison of appearance-based approaches, 7th International Conference on Digital Image Computing: Techniques and Applications, Sydney, Australia, Vol. 1, pp. 59–68.Search in Google Scholar

Huang, G.-B., Bai, Z., Kasun, L.L.C. and Vong, C.M. (2015). Local receptive fields based extreme learning machine, IEEE Computational Intelligence Magazine10(2): 18–29.10.1109/MCI.2015.2405316Search in Google Scholar

Huang, G.-B., Zhu, Q.-Y. and Siew, C.-K. (2004). Extreme learning machine: A new learning scheme of feedforward neural networks, International Joint Conference on Neural Networks, Budapest, Hungary, pp. 985–990.Search in Google Scholar

Huang, G.-B., Zhu, Q.-Y. and Siew, C.-K. (2006). Extreme learning machine: Theory and applications, Neurocomputing70(1–3): 489–501.10.1016/j.neucom.2005.12.126Search in Google Scholar

Jankowski, N. (2013). Meta-learning and new ways in model construction for classification problems, Journal of Network & Information Security4(4): 275–284.Search in Google Scholar

Jankowski, N. (2018). Comparison of prototype selection algorithms used in construction of neural networks learned by SVD, International Journal of Applied Mathematics and Computer Science28(4): 719–733, DOI: 10.2478/amcs-2018-0055.10.2478/amcs-2018-0055Open DOISearch in Google Scholar

Merz, C.J. and Murphy, P.M. (1998). UCI Repository of Machine Learning Databases, https://archive.ics.uci.edu/ml/index.php.Search in Google Scholar

Mitchell, T. (1997). Machine Learning, McGraw Hill, New York, NY.Search in Google Scholar

Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986). Learning internal representations by error propagation, in J.L.M.D.E. Rumelhart (Ed.), Parallel Distributed Processing: Explorations in Microstructure of Congnition, Vol. 1: Foundations, MIT Press, Cambridge, MA, pp. 318–362.Search in Google Scholar

Sovilj, D., Eirola, E., Miche, Y., Bjork, K.-M., Nian, R., Akusok, A. and Lendasse, A. (2016). Extreme learning machine for missing data using multiple imputations, Neurocomputing174(PA): 220–231.10.1016/j.neucom.2015.03.108Search in Google Scholar

Tang, J., Deng, C., Member, S. and Huang, G.-B. (2016). Extreme learning machine for multilayer perceptron, IEEE Transactions on Neural Networks and Learning Systems27(4): 809–821.10.1109/TNNLS.2015.242499525966483Search in Google Scholar

Tikhonov, A.N. and Arsenin, V.Y. (1977). Solutions of Ill-posed Problems, W.H. Winston, Washington, DC.Search in Google Scholar

Vapnik, V. (1995). The Nature of Statistical Learning Theory, Springer-Verlag, New York, NY.10.1007/978-1-4757-2440-0Search in Google Scholar

eISSN:
2083-8492
Sprache:
Englisch
Zeitrahmen der Veröffentlichung:
4 Hefte pro Jahr
Fachgebiete der Zeitschrift:
Mathematik, Angewandte Mathematik