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A new approach for functional data description is proposed in this paper. It consists of a regression model with a discrete hidden logistic process which is adapted for modeling curves with abrupt or smooth regime changes. The model parameters are estimated in a maximum likelihood framework through a dedicated expectation maximization (EM) algorithm. From the proposed generative model, a curve discrimination...
This paper presents a novel methodology to infer parameters of probabilistic models whose output noise is a Student-t distribution. The method is an extension of earlier work for models that are linear in parameters to non-linear multi-layer perceptrons (MLPs). We used an EM algorithm combined with variational approximation, an evidence procedure, and an optimisation algorithm. The technique was tested...
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