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Mixture models and applications

Bouguila, Nizar.

Mixture models and applications

Record Type:	Electronic resources : Monograph/item
Title/Author:	Mixture models and applicationsedited by Nizar Bouguila, Wentao Fan.
other author:	Bouguila, Nizar.
Published:	Cham :Springer International Publishing :2020.
Description:	xii, 355 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Data mining.
Online resource:	https://doi.org/10.1007/978-3-030-23876-6
ISBN:	9783030238766$q(electronic bk.)

Mixture models and applications
Mixture models and applications[electronic resource] /edited by Nizar Bouguila, Wentao Fan. - Cham :Springer International Publishing :2020. - xii, 355 p. :ill., digital ;24 cm. - Unsupervised and semi-supervised learning,2522-848X. - Unsupervised and semi-supervised learning..

A Gaussian Mixture Model Approach To Classifying Response Types -- Interactive Generation Of Calligraphic Trajectories From Gaussian Mixtures -- Mixture models for the analysis, edition, and synthesis of continuous time series -- Multivariate Bounded Asymmetric Gaussian Mixture Model -- Online Recognition Via A Finite Mixture Of Multivariate Generalized Gaussian Distributions -- L2 Normalized Data Clustering Through the Dirichlet Process Mixture Model of Von Mises Distributions with Localized Feature Selection -- Deriving Probabilistic SVM Kernels From Exponential Family Approximations to Multivariate Distributions for Count Data -- Toward an Efficient Computation of Log-likelihood Functions in Statistical Inference: Overdispersed Count Data Clustering -- A Frequentist Inference Method Based On Finite Bivariate And Multivariate Beta Mixture Models -- Finite Inverted Beta-Liouville Mixture Models with Variational Component Splitting -- Online Variational Learning for Medical Image Data Clustering -- Color Image Segmentation using Semi-Bounded Finite Mixture Models by Incorporating Mean Templates -- Medical Image Segmentation Based on Spatially Constrained Inverted Beta-Liouville Mixture Models -- Flexible Statistical Learning Model For Unsupervised Image Modeling And Segmentation.

This book focuses on recent advances, approaches, theories and applications related to mixture models. In particular, it presents recent unsupervised and semi-supervised frameworks that consider mixture models as their main tool. The chapters considers mixture models involving several interesting and challenging problems such as parameters estimation, model selection, feature selection, etc. The goal of this book is to summarize the recent advances and modern approaches related to these problems. Each contributor presents novel research, a practical study, or novel applications based on mixture models, or a survey of the literature. Reports advances on classic problems in mixture modeling such as parameter estimation, model selection, and feature selection; Present theoretical and practical developments in mixture-based modeling and their importance in different applications; Discusses perspectives and challenging future works related to mixture modeling.

ISBN: 9783030238766$q(electronic bk.)

Standard No.: 10.1007/978-3-030-23876-6doiSubjects--Topical Terms:

184440
Data mining.

LC Class. No.: QA76.9.D343 / M58 2020

Dewey Class. No.: 006.312

Mixture models and applications
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300 $a xii, 355 p. : $b ill., digital ; $c 24 cm.
490 1 $a Unsupervised and semi-supervised learning, $x 2522-848X
505 0 $a A Gaussian Mixture Model Approach To Classifying Response Types -- Interactive Generation Of Calligraphic Trajectories From Gaussian Mixtures -- Mixture models for the analysis, edition, and synthesis of continuous time series -- Multivariate Bounded Asymmetric Gaussian Mixture Model -- Online Recognition Via A Finite Mixture Of Multivariate Generalized Gaussian Distributions -- L2 Normalized Data Clustering Through the Dirichlet Process Mixture Model of Von Mises Distributions with Localized Feature Selection -- Deriving Probabilistic SVM Kernels From Exponential Family Approximations to Multivariate Distributions for Count Data -- Toward an Efficient Computation of Log-likelihood Functions in Statistical Inference: Overdispersed Count Data Clustering -- A Frequentist Inference Method Based On Finite Bivariate And Multivariate Beta Mixture Models -- Finite Inverted Beta-Liouville Mixture Models with Variational Component Splitting -- Online Variational Learning for Medical Image Data Clustering -- Color Image Segmentation using Semi-Bounded Finite Mixture Models by Incorporating Mean Templates -- Medical Image Segmentation Based on Spatially Constrained Inverted Beta-Liouville Mixture Models -- Flexible Statistical Learning Model For Unsupervised Image Modeling And Segmentation.
520 $a This book focuses on recent advances, approaches, theories and applications related to mixture models. In particular, it presents recent unsupervised and semi-supervised frameworks that consider mixture models as their main tool. The chapters considers mixture models involving several interesting and challenging problems such as parameters estimation, model selection, feature selection, etc. The goal of this book is to summarize the recent advances and modern approaches related to these problems. Each contributor presents novel research, a practical study, or novel applications based on mixture models, or a survey of the literature. Reports advances on classic problems in mixture modeling such as parameter estimation, model selection, and feature selection; Present theoretical and practical developments in mixture-based modeling and their importance in different applications; Discusses perspectives and challenging future works related to mixture modeling.
650 0 $a Data mining. $3 184440
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650 1 4 $a Signal, Image and Speech Processing. $3 273768
650 2 4 $a Probability and Statistics in Computer Science. $3 274053
650 2 4 $a Statistics and Computing/Statistics Programs. $3 275710
650 2 4 $a Probability Theory and Stochastic Processes. $3 274061
700 1 $a Bouguila, Nizar. $3 727908
700 1 $a Fan, Wentao. $3 884367
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773 0 $t Springer Nature eBook
830 0 $a Unsupervised and semi-supervised learning. $3 834422
856 4 0 $u https://doi.org/10.1007/978-3-030-23876-6
950 $a Engineering (SpringerNature-11647)