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Empirical Inference Conference Paper 2003

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

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Empirical Inference
Arthur Gretton

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.

Author(s): Gretton, A. and Desobry, .
Book Title: IEEE ICASSP Vol. 2
Journal: IEEE ICASSP Vol. 2
Pages: 709-712
Year: 2003
Month: April
Day: 0
Bibtex Type: Conference Paper (inproceedings)
Event Name: IEEE ICASSP
Digital: 0
Electronic Archiving: grant_archive
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik
Links:

BibTex 

@inproceedings{2134,
  title = {On-Line One-Class Support Vector Machines. An Application to Signal Segmentation},
  journal = {IEEE ICASSP Vol. 2},
  booktitle = {IEEE ICASSP Vol. 2},
  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.},
  pages = {709-712},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = apr,
  year = {2003},
  slug = {2134},
  author = {Gretton, A. and Desobry, .},
  month_numeric = {4}
}