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

Gretton, A; Desobry, F

doi:10.1109/ICASSP.2003.1202465

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

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Gretton, A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

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.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-DCB7-A

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.