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… (More)
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Topic mentions per year

Topic mentions per year

1975-2017
020040060019752017

Papers overview

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2013
2013
We present a noise-injected version of the Expectation-Maximization (EM) algorithm: the Noisy Expectation Maximization (NEM… (More)
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Highly Cited
2010
Highly Cited
2010
There are some situations when in the pattern recognition applications can appear some objects which are missing data. This thing… (More)
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Highly Cited
2004
Highly Cited
2004
The expectation-maximization (EM) method can facilitate maximizing likelihood functions that arise in statistical estimation… (More)
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Review
2002
Review
2002
This note represents my attempt at explaining the EM algorithm (Hartley, 1958; Dempster et al., 1977; McLachlan and Krishnan… (More)
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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… (More)
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Highly Cited
2001
Highly Cited
2001
The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR… (More)
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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… (More)
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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… (More)
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Highly Cited
1994
Highly Cited
1994
The algorithm described in this paper discovers one or more motifs in a collection of DNA or protein se quences by using the… (More)
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Highly Cited
1991
Highly Cited
1991
The expectation-maximization (EM) algorithm for maximizing likelihood functions, combined with the Viterbi algorithm, is applied… (More)
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