Pépite | Modèles de mélange pour la classification non supervisée de données qualitatives et mixtes

Key Words: — expectation maximization algorithm, fuzzy C-means algorithm, Gaussian mixture model, image segmentation, radiographic images,

A normal application of our classification process consists in the data acquisition, followed by the unsupervised training and supervised grouping of a few different models to

The idea of the Raftery and Dean approach is to divide the set of variables into the subset of relevant clustering variables and the subset of irrelevant variables, independent of

In this paper we present a method to learn finite Gaussian mixtures, based on the approximation result of Li and Barron (Li and Barron, 2000), which states that we can

By using fixed small (referring to the size of the determinant of the covariance matrix) candidate components, the method keeps inserting small components in high density areas while

Ophtalmologie Anatomie Pathologique Oto-Rhino-Laryngologie Gastro-Entérologie Chimie Analytique Anesthésie Réanimation Stomatologie et Chirurgie Maxillo-faciale Neurologie

We have improved the RJMCM method for model selection prediction of MGPs on two aspects: first, we extend the split and merge formula for dealing with multi-input regression, which

This manuscript reviews the justification for the conditional independence assumption in multivariate finite mixture models and summarizes what is known about the identifiability