Bouguila N., Fan W. (Eds.) Mixture Models and Applications

Springer, 2020. — 356 p. — (Unsupervised and Semi-Supervised Learning). — ISBN: ISBN: 978-3-030-23875-9.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.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
L 2 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