Reservoir size, spectral radius and connectivity in static classification problems

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

Volumn 5768 LNCS, Issue PART 1, 2009, Pages 1015-1024

  Alexandre, Luís A ^a   Embrechts, Mark J ^b  

^a UNIVERSITY OF BEIRA INTERIOR   (Portugal)

^b RENSSELAER POLYTECHNIC INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ERRORS; CLASSIFICATION PERFORMANCE; RESERVOIR COMPUTING; SPECTRAL RADII; STATIC CLASSIFICATION; STATIC PROBLEMS;

BACKPROPAGATION;

RECURRENT NEURAL NETWORKS;

EID: 70350608364     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04274-4_104     Document Type: Conference Paper

References (10)

1. 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)
2. 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)
3. Verstraeten, D., Schrauwen, B., D'Haene, M., Stroobandt, D.: An experimental unification of reservoir computing methods. Neural Networks 20, 391-403 (2007)
4. 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)
5. 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)
6. Legenstein, R., Maass, W.: Edge of chaos and prediction of computational performance for neural circuit models. Neural Networks 20, 323-334 (2007)
7. Wold, S., Sjöström, M., Eriksson, L.: PLS-regression: a basic tool of chemometrics. Chemo-metrics and Intelligent Laboratory Systems 58, 109-130 (2001)
8. 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)
9. Blake, C., Keogh, E., Merz, C.: UCI repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
10. Forina, M., Armanino, C.: Eigenvector projection and simplified nonlinear mapping of fatty acid content of italian olive oils. Ann. Chem. 72, 125-127 (1981)