Laurent Gudre

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We consider the problem of signal segmentation in the setup of supervised learning. The supervision lies here in the existence of labelled change points in a historical database of similar signals. Typical segmentation techniques rely on a penalized least square procedure where the smoothing parameter is fixed arbitrarily. We introduce the alpin (Adaptive(More)
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