« Expectation Maximization » : différence entre les versions

Dernière version du 29 janvier 2024 à 13:42

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-==en construction==
+#REDIRECTION[[Algorithme EM]]
-== Définition ==
+[[Catégorie:ENGLISH]]
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-== Français ==
-''' XXXXXXXXX '''
-== Anglais ==
+Voir [[algorithme EM]]
-''' Expectation Maximization'''
-Expectation maximization (EM) is an algorithm that finds the best estimates for model parameters when a dataset is missing information or has hidden latent variables. While this technique can be used to determine the maximum likelihood function, or the “best fit” model for a set of data, EM takes things a step further and works on incomplete data sets. This is achieved by inserting random values for the missing data points, and then estimating a second set of data. The new dataset is used to refine the guesses added to the first, with the process repeating until the algorithm’s termination criterion are met.
-<small>
 [https://deepai.org/machine-learning-glossary-and-terms/expectation-maximizationSource : DeepAI.org ]
-[[Catégorie:vocabulary]]