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| ==en construction==
| | #REDIRECTION[[Algorithme EM]] |
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| Voir [[algorithme EM]]
| | [[Catégorie:ENGLISH]] |
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| == Définition ==
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| == Français ==
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| ''' XXXXXXXXX '''
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| == Anglais ==
| | Voir [[algorithme EM]] |
| ''' Expectation Maximization'''
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| 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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| <small>
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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 ] |
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| [[Catégorie:DeepAI.org]]
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| [[Catégorie:vocabulary]]
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