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  2. EM algorithm and GMM model - Wikipedia

    en.wikipedia.org/wiki/EM_Algorithm_And_GMM_Model

    The EM algorithm consists of two steps: the E-step and the M-step. Firstly, the model parameters and the () can be randomly initialized. In the E-step, the algorithm tries to guess the value of () based on the parameters, while in the M-step, the algorithm updates the value of the model parameters based on the guess of () of the E-step.

  3. Expectation–maximization algorithm - Wikipedia

    en.wikipedia.org/wiki/Expectationmaximization...

    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. These parameter-estimates are then ...

  4. Determining the number of clusters in a data set - Wikipedia

    en.wikipedia.org/wiki/Determining_the_number_of...

    The average silhouette of the data is another useful criterion for assessing the natural number of clusters. The silhouette of a data instance is a measure of how closely it is matched to data within its cluster and how loosely it is matched to data of the neighboring cluster, i.e., the cluster whose average distance from the datum is lowest. [8]

  5. Model-based clustering - Wikipedia

    en.wikipedia.org/wiki/Model-based_clustering

    The parameters of the model, and for =, …,, are typically estimated by maximum likelihood estimation using the expectation-maximization algorithm (EM); see also EM algorithm and GMM model. Bayesian inference is also often used for inference about finite mixture models. [ 2 ]

  6. Naive Bayes classifier - Wikipedia

    en.wikipedia.org/wiki/Naive_Bayes_classifier

    This training algorithm is an instance of the more general expectationmaximization algorithm (EM): the prediction step inside the loop is the E-step of EM, while the re-training of naive Bayes is the M-step.

  7. Mixture of experts - Wikipedia

    en.wikipedia.org/wiki/Mixture_of_experts

    The mixture of experts, being similar to the gaussian mixture model, can also be trained by the expectation-maximization algorithm, just like gaussian mixture models. Specifically, during the expectation step, the "burden" for explaining each data point is assigned over the experts, and during the maximization step, the experts are trained to ...

  8. Uncommon Signs of Diabetes Doctors Want You to Know - AOL

    www.aol.com/uncommon-signs-diabetes-doctors-want...

    Endocrinologists share uncommon symptoms of diabetes that may indicate type 1, type 2, or prediabetes. Some signs include infections and dry skin.

  9. Mixture model - Wikipedia

    en.wikipedia.org/wiki/Mixture_model

    A typical finite-dimensional mixture model is a hierarchical model consisting of the following components: . N random variables that are observed, each distributed according to a mixture of K components, with the components belonging to the same parametric family of distributions (e.g., all normal, all Zipfian, etc.) but with different parameters