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

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons83946

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

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Citation

Gretton, A. (2003). On-Line One-Class Support Vector Machines. An Application to Signal Segmentation. IEEE ICASSP Vol. 2, 709-712.


Cite as: http://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.