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Qu'est-ce (qui) est EM algorithm and GMM model - définition
EM algorithm and GMM model
In statistics, EM (expectation maximization) algorithm handles latent variables, while GMM is the Gaussian mixture model.
Expectation–maximization algorithm
![An animation demonstrating the EM algorithm fitting a two component Gaussian [[mixture model]] to the [[Old Faithful]] dataset. The algorithm steps through from a random initialization to convergence.](https://commons.wikimedia.org/wiki/Special:FilePath/Em old faithful.gif?width=200 "An animation demonstrating the EM algorithm fitting a two component Gaussian [[mixture model]] to the [[Old Faithful]] dataset. The algorithm steps through from a random initialization to convergence.")
ITERATIVE METHOD FOR FINDING MAXIMUM LIKELIHOOD ESTIMATES IN STATISTICAL MODELS
Expectation-Maximization; EM algorithm; Em algorithm; Expectation-maximisation algorithm; Expectation-maximization; Expectation Maximization; EM Algorithm; Expectation-Maximization Clustering; Expectation maximization algorithm; Expectation maximization; Expectation-maximisation; Expectation maximisation; Expectation maximization method; Expectation-maximization method; EM-algorithm; Expectation-maximization algorithm; EM clustering; Expectation Maximisation; Expectation maximization principle
In statistics, an expectation–maximization (EM) algorithm is an iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step.
Bitap algorithm
APPROXIMATE STRING MATCHING ALGORITHM
Shift-or algorithm; Wu-Manber; Shift Or Algorithm; Baeza-Yates-Gonnet algorithm; Shift or algorithm; Shift-or; Shift-and; Baeza-Yates-Gonnet; Bitap; Shift-Or; Shift Or; Shift or; Shift Or algorithm
The bitap algorithm (also known as the shift-or, shift-and or Baeza-Yates–Gonnet algorithm) is an approximate string matching algorithm. The algorithm tells whether a given text contains a substring which is "approximately equal" to a given pattern, where approximate equality is defined in terms of Levenshtein distance if the substring and pattern are within a given distance k of each other, then the algorithm considers them equal.
