Abstract
Reservoir computing is a recent paradigm that has proved to be quite effective given the classical difficulty in training recurrent neural networks. An approach to using reservoir recurrent neural networks has been recently proposed for static problems and in this paper we look at the influence of the reservoir size, spectral radius and connectivity on the classification error in these problems. The main conclusion derived from the performed experiments is that only the size of the reservoir is relevant with the spectral radius and the connectivity of the reservoir not affecting the classification performance.
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References
Jaeger, H.: ‘The echo state’ approach to analysing and training recurrent neural networks. Technical Report GMD Report 148, Fraunhofer Institute for Autonomous Intelligent Systems (2001)
Maas, W., Natschläger, T., Markram, H.: Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation 14(11), 2531–2560 (2002)
Verstraeten, D., Schrauwen, B., D’Haene, M., Stroobandt, D.: An experimental unification of reservoir computing methods. Neural Networks 20, 391–403 (2007)
Embrechts, M., Alexandre, L., Linton, J.: Reservoir computing for static pattern recognition. In: 17th European Symposium on Artificial Neural Networks – ESANN 2009, Bruges, Belgium (2009)
Alexandre, L., Embrechts, M., Linton, J.: Benchmarking reservoir computing on time-independent classification tasks. In: International Joint Conference on Neural Networks – IJCNN 2009, Atlanta, Georgia, USA, pp. 89–93 (2009)
Legenstein, R., Maass, W.: Edge of chaos and prediction of computational performance for neural circuit models. Neural Networks 20, 323–334 (2007)
Wold, S., Sjöström, M., Eriksson, L.: PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems 58, 109–130 (2001)
Anderson, E., Bai, Z., Bischof, C., Blackford, S., Demmel, J., Dongarra, J., Du Croz, J., Greenbaum, A., Hammarling, S., McKenney, A., Sorensen, D.: LAPACK Users’ Guide, 3rd edn. Society for Industrial and Applied Mathematics, Philadelphia (1999)
Blake, C., Keogh, E., Merz, C.: UCI repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Forina, M., Armanino, C.: Eigenvector projection and simplified nonlinear mapping of fatty acid content of italian olive oils. Ann. Chem. 72, 125–127 (1981)
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Alexandre, L.A., Embrechts, M.J. (2009). Reservoir Size, Spectral Radius and Connectivity in Static Classification Problems. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04274-4_104
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DOI: https://doi.org/10.1007/978-3-642-04274-4_104
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04273-7
Online ISBN: 978-3-642-04274-4
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