Extending reservoir computing with random static projections: A hybrid between extreme learning and RC

Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010

Volumn , Issue , 2010, Pages 303-308

  Butcher, John ^a   Verstraeten, David ^b   Schrauwen, Benjamin ^b   Day, Charles ^a   Haycock, Peter ^a  

^a KEELE UNIVERSITY   (United Kingdom)

^b GHENT UNIVERSITY   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL REQUIREMENTS; DIGIT RECOGNITION; EXTREME LEARNING MACHINE; HYBRID ARCHITECTURES; MAPPING DATA; NON-LINEAR TRANSFORMATIONS; ORDER POLYNOMIALS; RESERVOIR COMPUTING;

ARTIFICIAL INTELLIGENCE; RECURRENT NEURAL NETWORKS;

LEARNING SYSTEMS;

EID: 79953702893     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (6)

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3. H. Jaeger. The "echo state" approach to analysing and training recurrent neural networks. Technical report, German National Research Institute for Computer Science, 2001.
4. X. Dutoit. Reservoir Computing for Intelligent Mobile Systems. PhD thesis, Department of Mechanical Engineering, Katholleke Universiteit Leuven, Belgium, 2009.
5. G.B. Huang, Q.Y. Zhu, and C.H. Siew. Extreme learning machines: Theory and applications. Neurocomputing, 70:489-501, 2006.
6. Y. Miche, A. Sorajamaa, P. Bas, O. Simula, C. Jutten, and A. Lendasse. OP-ELM: Optimally pruned extreme learning machine. IEEE Transactions on Neural Networks, 21(1):158-162, December 2009. 308