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.
Recenzijas
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)
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.
Nizar Bouguila received the engineer degree from the University of Tunis, Tunis, Tunisia, in 2000, and the M.Sc. and Ph.D. degrees in computer science from Sherbrooke University, Sherbrooke, QC, Canada, in 2002 and 2006, respectively. He is currently a Professor with the Concordia Institute for Information Systems Engineering (CIISE) at Concordia University, Montreal, Quebec, Canada. His research interests include image processing, machine learning, data mining, computer vision, and pattern recognition. Prof. Bouguila received the best Ph.D Thesis Award in Engineering and Natural Sciences from Sherbrooke University in 2007. He was awarded the prestigious Prix dexcellence de lassociation des doyens des etudes superieures au Quebec (best Ph.D Thesis Award in Engineering and Natural Sciences in Quebec), and was a runner-up for the prestigious NSERC doctoral prize. He is the author or co-author of more than 200 publications in several prestigious journals and conferences. Heis a regular reviewer for many international journals and serving as associate editor for several journals such as Pattern Recognition. Dr. Bouguila is a licensed Professional Engineer registered in Ontario, and a Senior Member of the IEEE. He is the holder of the Concordia University Research Chair. 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.
