  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 rule is derived using the Maximum A Posteriori rule. The
proposed model is evaluated using simulated curves and real world curves
acquired during railway switch operations, by performing comparisons with the
piecewise regression approach in terms of curve modeling and classification.

Methodology;Machine Learning;Machine Learning