Mixture Models and Applications, 1st ed. 2020 Unsupervised and Semi-Supervised Learning Series

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.