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


Conference Paper


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

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)


Digital: 0
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PostScript


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