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The Noisy Expectation Maximization Algorithm
"... � We present a noise-injected version of the Expectation-Maximization (EM) algorithm: the Noisy Expectation Maximization (NEM) algorithm. � The NEM theorem shows that additive noise speeds up the average convergence of the EM algorithm to a local maximum of the likelihood surface if a positivity con ..."
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� We present a noise-injected version of the Expectation-Maximization (EM) algorithm: the Noisy Expectation Maximization (NEM) algorithm. � The NEM theorem shows that additive noise speeds up the average convergence of the EM algorithm to a local maximum of the likelihood surface if a positivity
The Expectation Maximization Algorithm
, 2002
"... This note represents my attempt at explaining the EM algorithm (Hartley, 1958; Dempster et al., 1977; McLachlan and Krishnan, 1997). This is just a slight variation on Tom Minka's tutorial (Minka, 1998), perhaps a little easier (or perhaps not). It includes a graphical example to provide some i ..."
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This note represents my attempt at explaining the EM algorithm (Hartley, 1958; Dempster et al., 1977; McLachlan and Krishnan, 1997). This is just a slight variation on Tom Minka's tutorial (Minka, 1998), perhaps a little easier (or perhaps not). It includes a graphical example to provide some
Inductive Programming by Expectation Maximization Algorithm
"... This paper proposes an algorithm which can write programs automatically to solve problems. We model the sequence of instructions as a n-gram language model and the sequence is represented by some hidden variables. Expectation maximization algorithm is applied to train the n-gram model and perform pr ..."
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This paper proposes an algorithm which can write programs automatically to solve problems. We model the sequence of instructions as a n-gram language model and the sequence is represented by some hidden variables. Expectation maximization algorithm is applied to train the n-gram model and perform
Expectation-Maximization Algorithm with Local Adaptivity ∗
"... Abstract. We develop an expectation-maximization algorithm with local adaptivity for image segmentation and classification. The key idea of our approach is to combine global statistics extracted from the Gaussian mixture model or other proper statistical models with local statistics and geometrical ..."
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Abstract. We develop an expectation-maximization algorithm with local adaptivity for image segmentation and classification. The key idea of our approach is to combine global statistics extracted from the Gaussian mixture model or other proper statistical models with local statistics and geometrical
Space-Alternating Generalized Expectation-Maximization Algorithm
- IEEE Trans. Signal Processing
, 1994
"... The expectation-maximization (EM) method can facilitate maximizing likelihood functions that arise in statistical estimation problems. In the classical EM paradigm, one iteratively maximizes the conditional loglikelihood of a single unobservable complete data space, rather than maximizing the intra ..."
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Cited by 192 (27 self)
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The expectation-maximization (EM) method can facilitate maximizing likelihood functions that arise in statistical estimation problems. In the classical EM paradigm, one iteratively maximizes the conditional loglikelihood of a single unobservable complete data space, rather than maximizing
Expectation Maximization Algorithms for Conditional Likelihoods
- Proceedings of the 22nd International Conference on Machine Learning (ICML-2005
, 2005
"... We introduce an expectation maximizationtype (EM) algorithm for maximum likelihood optimization of conditional densities. It is applicable to hidden variable models where the distributions are from the exponential family. The algorithm can alternatively be viewed as automatic step size selection for ..."
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Cited by 25 (5 self)
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We introduce an expectation maximizationtype (EM) algorithm for maximum likelihood optimization of conditional densities. It is applicable to hidden variable models where the distributions are from the exponential family. The algorithm can alternatively be viewed as automatic step size selection
Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm
- IEEE TRANSACTIONS ON MEDICAL. IMAGING
, 2001
"... The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic limi ..."
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Cited by 637 (14 self)
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methods are limited to using MRF as a general prior in an FM model-based approach. To fit the HMRF model, an EM algorithm is used. We show that by incorporating both the HMRF model and the EM algorithm into a HMRF-EM framework, an accurate and robust segmentation can be achieved. More importantly
Word Alignment and the Expectation-Maximization Algorithm
"... The purpose of this tutorial is to give you an example of how to take a simple discrete probabilistic model and derive the expectation maximization updates for it and then turn them into code. We give some examples and identify the key ideas that make the algorithms work. These are meant to be as in ..."
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The purpose of this tutorial is to give you an example of how to take a simple discrete probabilistic model and derive the expectation maximization updates for it and then turn them into code. We give some examples and identify the key ideas that make the algorithms work. These are meant
Expectation-Maximization Algorithm and Image Segmentation
"... In computer vision, image segmentation problem is to partition a digital image into multiple parts. The goal is to change the representation of the image and make it more meaningful and easier to analyze [11]. In this assignment, we will show how an image segmentation algorithm works in a real appli ..."
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In computer vision, image segmentation problem is to partition a digital image into multiple parts. The goal is to change the representation of the image and make it more meaningful and easier to analyze [11]. In this assignment, we will show how an image segmentation algorithm works in a real
Results 1 - 10
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