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

John Butcher, David Verstraeten UGent, Benjamin Schrauwen, Charles Day and Peter Haycock (2010) European Symposium on Artificial Neural Networks, 18th, Proceedings. p.303-308
abstract
Reservoir Computing is a relatively new paradigm in the field of neural networks that has shown promise in applications where traditional recurrent neural networks have performed poorly. The main advantage of using reservoirs is that only the output weights are trained, reducing computational requirements significantly. There is a trade-off, however, between the amount of memory a reservoir can possess and its capability of mapping data into a highly non-linear transformation space. A new, hybrid architecture, combining a reservoir with an extreme learning machine, is presented which overcomes this trade-off, whose performance is demonstrated on a 4th order polynomial modelling task and an isolated spoken digit recognition task.
Please use this url to cite or link to this publication:
author
organization
year
type
conference
publication status
published
subject
keyword
machine learning, neural networks, Reservoir Computing
in
European Symposium on Artificial Neural Networks, 18th, Proceedings
pages
303 - 308
publisher
D-Side
place of publication
Evere, Belgium
conference name
18th European Symposium on Artificial Neural Networks (ESANN 2010)
conference location
Bruges, Belgium
conference start
2010-04-28
conference end
2010-04-30
language
English
UGent publication?
yes
classification
C1
copyright statement
I have transferred the copyright for this publication to the publisher
id
1024136
handle
http://hdl.handle.net/1854/LU-1024136
date created
2010-08-16 15:35:44
date last changed
2017-01-02 09:52:39
@inproceedings{1024136,
  abstract     = {Reservoir Computing is a relatively new paradigm in the field of neural networks that has shown promise in applications where traditional recurrent neural networks have performed poorly. The main advantage of using reservoirs is that only the output weights are trained, reducing computational requirements significantly. There is a trade-off, however, between the amount of memory a reservoir can possess and its capability of mapping data into a highly non-linear transformation space. A new, hybrid architecture, combining a reservoir with an extreme learning machine, is presented which overcomes this trade-off, whose performance is demonstrated on a 4th order polynomial modelling task and an isolated spoken digit recognition task.},
  author       = {Butcher, John and Verstraeten, David and Schrauwen, Benjamin and Day, Charles and Haycock, Peter},
  booktitle    = {European Symposium on Artificial Neural Networks, 18th, Proceedings},
  keyword      = {machine learning,neural networks,Reservoir Computing},
  language     = {eng},
  location     = {Bruges, Belgium},
  pages        = {303--308},
  publisher    = {D-Side},
  title        = {Extending reservoir computing with random static projections: a hybrid between extreme learning and RC},
  year         = {2010},
}

Chicago
Butcher, John, David Verstraeten, Benjamin Schrauwen, Charles Day, and Peter Haycock. 2010. “Extending Reservoir Computing with Random Static Projections: a Hybrid Between Extreme Learning and RC.” In European Symposium on Artificial Neural Networks, 18th, Proceedings, 303–308. Evere, Belgium: D-Side.
APA
Butcher, J., Verstraeten, D., Schrauwen, B., Day, C., & Haycock, P. (2010). Extending reservoir computing with random static projections: a hybrid between extreme learning and RC. European Symposium on Artificial Neural Networks, 18th, Proceedings (pp. 303–308). Presented at the 18th European Symposium on Artificial Neural Networks (ESANN 2010), Evere, Belgium: D-Side.
Vancouver
1.
Butcher J, Verstraeten D, Schrauwen B, Day C, Haycock P. Extending reservoir computing with random static projections: a hybrid between extreme learning and RC. European Symposium on Artificial Neural Networks, 18th, Proceedings. Evere, Belgium: D-Side; 2010. p. 303–8.
MLA
Butcher, John, David Verstraeten, Benjamin Schrauwen, et al. “Extending Reservoir Computing with Random Static Projections: a Hybrid Between Extreme Learning and RC.” European Symposium on Artificial Neural Networks, 18th, Proceedings. Evere, Belgium: D-Side, 2010. 303–308. Print.