This paper proposes a method of segmenting temporal data into ordered
classes. It is based on mixture models and a discrete latent process, which
enables to successively activates the classes...
The classification can be
performed by maximizing the likelihood via the EM algorithm or by
simultaneously optimizing the model parameters and the partition by the CEM
algorithm. These two algorithms can be seen as alternatives to Fisher's
algorithm, which improve its computing time.