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- aggregation classification "C1".
- aggregation creator B40977.
- aggregation creator B40978.
- aggregation creator B40979.
- aggregation creator person.
- aggregation creator person.
- aggregation date "2010".
- aggregation format "application/pdf".
- aggregation hasFormat 1024136.bibtex.
- aggregation hasFormat 1024136.csv.
- aggregation hasFormat 1024136.dc.
- aggregation hasFormat 1024136.didl.
- aggregation hasFormat 1024136.doc.
- aggregation hasFormat 1024136.json.
- aggregation hasFormat 1024136.mets.
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- aggregation hasFormat 1024136.xls.
- aggregation hasFormat 1024136.yaml.
- aggregation language "eng".
- aggregation publisher "D-Side".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Technology and Engineering".
- aggregation title "Extending reservoir computing with random static projections: a hybrid between extreme learning and RC".
- aggregation 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.".
- aggregation authorList BK103675.
- aggregation endPage "308".
- aggregation startPage "303".
- aggregation aggregates 1024140.
- aggregation isDescribedBy 1024136.
- aggregation similarTo LU-1024136.