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This paper introduces the novel application of support vector machine for filtering of time-series signal corrupted by Gaussian and non-Gaussian noise. In real world, the case of non-Gaussian noise (e.g. impulse noise, signal dependent noise) is very common. The optimal Wiener filter, which is a linear approach, can yield good results to Gaussian white noise, but performs poorly in case of non-Gaussian...
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