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Mixture Models and Applications

Overview of attention for book
Cover of 'Mixture Models and Applications'

Table of Contents

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    Book Overview
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    Chapter 1 A Gaussian Mixture Model Approach to Classifying Response Types
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    Chapter 2 Interactive Generation of Calligraphic Trajectories from Gaussian Mixtures
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    Chapter 3 Mixture Models for the Analysis, Edition, and Synthesis of Continuous Time Series
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    Chapter 4 Multivariate Bounded Asymmetric Gaussian Mixture Model
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    Chapter 5 Online Recognition via a Finite Mixture of Multivariate Generalized Gaussian Distributions
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    Chapter 6 L 2 Normalized Data Clustering Through the Dirichlet Process Mixture Model of von Mises Distributions with Localized Feature Selection
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    Chapter 7 Deriving Probabilistic SVM Kernels from Exponential Family Approximations to Multivariate Distributions for Count Data
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    Chapter 8 Toward an Efficient Computation of Log-Likelihood Functions in Statistical Inference: Overdispersed Count Data Clustering
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    Chapter 9 A Frequentist Inference Method Based on Finite Bivariate and Multivariate Beta Mixture Models
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    Chapter 10 Finite Inverted Beta-Liouville Mixture Models with Variational Component Splitting
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    Chapter 11 Online Variational Learning for Medical Image Data Clustering
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    Chapter 12 Color Image Segmentation Using Semi-bounded Finite Mixture Models by Incorporating Mean Templates
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    Chapter 13 Medical Image Segmentation Based on Spatially Constrained Inverted Beta-Liouville Mixture Models
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    Chapter 14 Flexible Statistical Learning Model for Unsupervised Image Modeling and Segmentation
Attention for Chapter 14: Flexible Statistical Learning Model for Unsupervised Image Modeling and Segmentation
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Citations

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Chapter title
Flexible Statistical Learning Model for Unsupervised Image Modeling and Segmentation
Chapter number 14
Book title
Mixture Models and Applications
Published by
Springer, Cham, January 2020
DOI 10.1007/978-3-030-23876-6_14
Book ISBNs
978-3-03-023875-9, 978-3-03-023876-6
Authors

Ines Channoufi, Fatma Najar, Sami Bourouis, Muhammad Azam, Alrence S. Halibas, Roobaea Alroobaea, Ali Al-Badi, Channoufi, Ines, Najar, Fatma, Bourouis, Sami, Azam, Muhammad, Halibas, Alrence S., Alroobaea, Roobaea, Al-Badi, Ali

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 33%
Professor 1 33%
Other 1 33%
Readers by discipline Count As %
Unspecified 1 33%
Computer Science 1 33%
Engineering 1 33%

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