enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  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...

    Itself can be extended into the Expectation conditional maximization either (ECME) algorithm. [35] This idea is further extended in generalized expectation maximization (GEM) algorithm, in which is sought only an increase in the objective function F for both the E step and M step as described in the As a maximizationmaximization procedure ...

  4. Baum–Welch algorithm - Wikipedia

    en.wikipedia.org/wiki/Baum–Welch_algorithm

    In electrical engineering, statistical computing and bioinformatics, the Baum–Welch algorithm is a special case of the expectationmaximization algorithm used to find the unknown parameters of a hidden Markov model (HMM). It makes use of the forward-backward algorithm to compute the statistics for the expectation step. The Baum–Welch ...

  5. Multiple EM for Motif Elicitation - Wikipedia

    en.wikipedia.org/wiki/Multiple_EM_for_Motif...

    Expectation maximization (EM). EM based heuristic for choosing the EM starting point. Maximum likelihood ratio based (LRT-based) heuristic for determining the best number of model-free parameters. Multi-start for searching over possible motif widths. Greedy search for finding multiple motifs. However, one often doesn't know where the starting ...

  6. Expectation Maximisation - Wikipedia

    en.wikipedia.org/?title=Expectation_Maximisation&...

    Download as PDF; Printable version; ... move to sidebar hide. From Wikipedia, the free encyclopedia. Redirect page. Redirect to: Expectationmaximization algorithm;

  7. MM algorithm - Wikipedia

    en.wikipedia.org/wiki/Mm_algorithm

    The MM algorithm is an iterative optimization method which exploits the convexity of a function in order to find its maxima or minima. The MM stands for “Majorize-Minimization” or “Minorize-Maximization”, depending on whether the desired optimization is a minimization or a maximization.

  8. Image segmentation - Wikipedia

    en.wikipedia.org/wiki/Image_segmentation

    The expectationmaximization algorithm is utilized to iteratively estimate the a posterior probabilities and distributions of labeling when no training data is available and no estimate of segmentation model can be formed. A general approach is to use histograms to represent the features of an image and proceed as outlined briefly in this ...

  9. Softmax function - Wikipedia

    en.wikipedia.org/wiki/Softmax_function

    The Softmax function is a smooth approximation to the arg max function: the function whose value is the index of a vector's largest element. The name "softmax" may be misleading.