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Relaxations de la régression logistique : modèles pour l'apprentissage sur une sous-population et la prédiction sur une autre., and . DMAS, volume A-1 of RNTI, page 200-212. Cépaduès-Éditions, (2005)A predictive deviance criterion for selecting a generative model in semi-supervised classification., , , and . Comput. Stat. Data Anal., (2013)Interpretable domain adaptation using unsupervised feature selection on pre-trained source models., , , and . Neurocomputing, (2022)Gaussian-Based Visualization of Gaussian and Non-Gaussian-Based Clustering., , and . J. Classif., 38 (1): 129-157 (2021)Parameter Setting for Evolutionary Latent Class Clustering., , , , and . ISICA, volume 4683 of Lecture Notes in Computer Science, page 472-484. Springer, (2007)Initializing EM using the properties of its trajectories in Gaussian mixtures.. Stat. Comput., 14 (3): 267-279 (2004)Pourquoi les modèles de mélange pour la classification ?. Monde des Util. Anal. Données, (2009)Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models., , and . Comput. Stat. Data Anal., 41 (3-4): 561-575 (2003)Relaxing the Identically Distributed Assumption in Gaussian Co-Clustering for High Dimensional Data., , and . CoRR, (2018)Model-based clustering of multivariate ordinal data relying on a stochastic binary search algorithm., and . Stat. Comput., 26 (5): 929-943 (2016)