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  On-Line One-Class Support Vector Machines. An Application to Signal Segmentation

Gretton, A., & Desobry, F. (2003). On-Line One-Class Support Vector Machines. An Application to Signal Segmentation. In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '03) (pp. 709-712). Piscataway, NJ, USA: IEEE.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-DCB7-A Version Permalink: http://hdl.handle.net/21.11116/0000-0005-71DB-5
Genre: Conference Paper

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 Creators:
Gretton, A1, 2, Author              
Desobry, F, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: In this paper, we describe an efficient algorithm to sequentially update a density support estimate obtained using one-class support vector machines. The solution provided is an exact solution, which proves to be far more computationally attractive than a batch approach. This deterministic technique is applied to the problem of audio signal segmentation, with simulations demonstrating the computational performance gain on toy data sets, and the accuracy of the segmentation on audio signals.

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 Dates: 2003-04
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 2134
DOI: 10.1109/ICASSP.2003.1202465
 Degree: -

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Title: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2003)
Place of Event: Hong Kong, China
Start-/End Date: 2003-04-06 - 2003-04-10

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Title: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '03)
Source Genre: Proceedings
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Affiliations:
Publ. Info: Piscataway, NJ, USA : IEEE
Pages: - Volume / Issue: 2 Sequence Number: - Start / End Page: 709 - 712 Identifier: ISBN: 0-7803-7663-3