expectation-maximization algorithm
[ɪk'spɛktəʃən mæksɪmaɪ'zeɪʃən 'ælɡərɪðəm]
nounpl: expectation-maximization algorithms
algoritmo de maximização de esperança
1. A statistical algorithm used to find maximum likelihood estimates of parameters in probabilistic models, particularly when dealing with latent (hidden) variables or incomplete data
The expectation-maximization algorithm is widely used in machine learning to cluster data when the true class labels are unknown.
O algoritmo de maximização de esperança é amplamente utilizado em aprendizado de máquina para agrupar dados quando os rótulos de classe verdadeiros são desconhecidos.
2. An iterative procedure that alternates between an expectation step (E-step) that estimates missing data and a maximization step (M-step) that updates parameter estimates
In the E-step of the expectation-maximization algorithm, we compute the expected value of the log-likelihood function given the current parameter estimates.
Na etapa E do algoritmo de maximização de esperança, calculamos o valor esperado da função log-verossimilhança dado as estimativas de parâmetros atuais.
3. A fundamental technique in statistical computing used for problems such as Gaussian mixture modeling, hidden Markov models, and data imputation
The expectation-maximization algorithm converges to a local maximum of the likelihood function.
O algoritmo de maximização de esperança converge para um máximo local da função de verossimilhança.
The expectation-maximization algorithm is a cornerstone technique in machine learning and statistics education worldwide. In Brazil and Portugal, it is primarily discussed in academic and professional computing contexts. The term is typically not abbreviated in formal technical writing in Portuguese, though the English abbreviation 'EM' is often used even in Portuguese texts due to its widespread adoption in the international scientific community.
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