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  Optimized Support Vector Machines for Nonstationary Signal Classification

Davy, M., Gretton, A., Doucet, A., & Rayner, P. (2002). Optimized Support Vector Machines for Nonstationary Signal Classification. IEEE Signal Processing Letters, 9(12), 442-445. doi:10.1109/LSP.2002.806070.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-DE28-D Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-DE29-B
Genre: Journal Article

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 Creators:
Davy, M, Author
Gretton, A1, Author              
Doucet, A, Author
Rayner, PJW, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: This letter describes an efficient method to perform nonstationary signal classification. A support vector machine (SVM) algorithm is introduced and its parameters optimised in a principled way. Simulations demonstrate that our low complexity method outperforms state-of-the-art nonstationary signal classification techniques.

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 Dates: 2002-12
 Publication Status: Published in print
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Title: IEEE Signal Processing Letters
Source Genre: Journal
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Pages: - Volume / Issue: 9 (12) Sequence Number: - Start / End Page: 442 - 445 Identifier: -