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Reference TypeConference Paper (Conference Proceedings)
Last Update2015: (UTC)
Metadata Last Update2016: (UTC) administrator
Citation KeyJalalvandDemNevVanMar:2015:DeReCo
TitleDesign of reservoir computing systems for noise-robust speech and handwriting recognition
Access Date2021, Dec. 03
Secondary TypeDoctoral Work
Number of Files1
Size1471 KiB
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Author1 Jalalvand, Azarakhsh
2 Demuynck, Kris
3 De Neve, Wesley
4 Van de Walle, Rik
5 Martens, Jean-Pierre
Affiliation1 Ghent University - iMinds
2 Ghent University - iMinds
3 Ghent University - iMinds
4 Ghent University - iMinds
5 Ghent University - iMinds
EditorSegundo, Maurício Pamplona
Faria, Fabio Augusto
Conference NameConference on Graphics, Patterns and Images, 28 (SIBGRAPI)
Conference LocationSalvador
DateAug. 26-29, 2015
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeMaster's or Doctoral Work
History (UTC)2015-07-31 20:05:18 :: -> administrator ::
2016-06-03 21:18:38 :: administrator -> :: 2015
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Keywordsreservoir computing networks
speech processing
image processing
artificial neural networks
noise robustness
AbstractIn this work, we address the noise robustness of the pattern recognition systems by investigating the application of Reservoir Computing Networks (RCNs) on speech and image recognition tasks. Our work introduces different architectures of RCN-based systems along with a coherent task-independent strategy to optimize the reservoir parameters. We show that such systems are more robust that the state-of-the-arts in the presence of noise and RCNs can be used for both robust recognition tasks as well as denoising approaches. Moreover, the successful application of RCNs on different tasks using the proposed strategy supports our claim that it is task-independent. > SDLA > SIBGRAPI 2015 > Design of reservoir...
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Next Higher Units8JMKD3MGPBW34M/3K24PF8
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