Mixture Models and Applications
Description
- 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 tomixture modeling.
Reviews / Votes
"This book can be taken as a review of the subject. It is also a very good starting point for understanding mixture modeling and even for setting up new research. I strongly recommend this work for researchers and advanced undergraduate and graduate students of computer science and applied probability." (Arturo Ortiz-Tapia, Computing Reviews, January 18, 2021)
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Persons
Wentao Fan received his M.Sc. and Ph.D. degrees in electrical and computer engineering from Concordia University, Montreal, Quebec, Canada, in 2009 and 2014, respectively. He is currently an Associate Professor in the Department of Computer Science and Technology, Huaqiao University, Xiamen, China. His research interests include machine learning, computer vision, deep learning and pattern recognition.
Content
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.