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Expectation–maximization algorithm

Known as: Expectation Maximization, EM clustering, Expectation maximization method 
In statistics, an expectation–maximization (EM) algorithm is an iterative method for finding maximum likelihood or maximum a posteriori (MAP… Expand
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Papers overview

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Review
2019
Review
2019
Due to the malicious attacks in wireless networks, physical layer security has attracted increasing concerns from both academia… Expand
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Highly Cited
2015
Highly Cited
2015
Models for the processes by which ideas and influence propagate through a social network have been studied in a number of domains… Expand
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Highly Cited
2010
Highly Cited
2010
  • Charles Elkan
  • Encyclopedia of Machine Learning
  • 2010
  • Corpus ID: 11846408
In this paper we take a look at the Expectation Maximization algorithm and an example of its use in a real world applications. 
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Highly Cited
2005
Highly Cited
2005
We address the problem of regional color transfer between two natural images by probabilistic segmentation. We use a new… Expand
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Highly Cited
2002
Highly Cited
2002
Retrieving images from large and varied collections using image content as a key is a challenging and important problem. We… Expand
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Highly Cited
1997
Highly Cited
1997
A new information-theoretic approach is presented for finding the pose of an object in an image. The technique does not require… Expand
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Highly Cited
1996
Highly Cited
1996
A common task in signal processing is the estimation of the parameters of a probability distribution function. Perhaps the most… Expand
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Highly Cited
1995
Highly Cited
1995
The maximum likelihood (ML) expectation maximization (EM) approach in emission tomography has been very popular in medical… Expand
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Highly Cited
1995
Highly Cited
1995
The MEME algorithm extends the expectation maximization (EM) algorithm for identifying motifs in unaligned biopolymer sequences… Expand
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Highly Cited
1994
Highly Cited
1994
The expectation-maximization (EM) method can facilitate maximizing likelihood functions that arise in statistical estimation… Expand
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