« Expectation Maximization » : différence entre les versions


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#REDIRECTION[[Algorithme EM]]


== Définition ==
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== Français ==
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== 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.
 
 
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[https://deepai.org/machine-learning-glossary-and-terms/expectation-maximizationSource : DeepAI.org ]
[https://deepai.org/machine-learning-glossary-and-terms/expectation-maximizationSource : DeepAI.org ]
[[Catégorie:vocabulary]]

Dernière version du 29 janvier 2024 à 12:42

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Voir algorithme EM


: DeepAI.org

Récupérée de « https://datafranca.org/wiki/index.php?title=Expectation_Maximization&oldid=95374 »

Contributeurs: Claire Gorjux, wiki