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/Expectation–maximization...

   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 maximization–maximization procedure ...

4. 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: Expectation–maximization algorithm;

5. 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.

6. 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 ...

7. 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]

8. Learn more about AOL's Free Email with AOL Mail | AOL Products

   www.aol.com/products/utilities/aol-mail

   Keep your inbox clutter-free with automated tools. See all emails based on topic (e.g. photos), with contextual bonus features with the new Views functionality. Or, use advanced filters to sort ...

9. Ordered subset expectation maximization - Wikipedia

   en.wikipedia.org/wiki/Ordered_subset_expectation...

   In mathematical optimization, the ordered subset expectation maximization (OSEM) method is an iterative method that is used in computed tomography.. In applications in medical imaging, the OSEM method is used for positron emission tomography (PET), for single-photon emission computed tomography (SPECT), and for X-ray computed tomography (X-ray CT) scans.