Overview
- 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
Part of the book series: Unsupervised and Semi-Supervised Learning (UNSESUL)
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Table of contents (14 chapters)
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Generalized Gaussian-Based Models
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Spherical and Count Data Clustering
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Bounded and Semi-bounded Data Clustering
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Image Modeling and Segmentation
Keywords
About this book
- 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
“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)
“[T]his … is … 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.” (Computing Reviews)Editors and Affiliations
About the editors
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.
Bibliographic Information
Book Title: Mixture Models and Applications
Editors: Nizar Bouguila, Wentao Fan
Series Title: Unsupervised and Semi-Supervised Learning
DOI: https://doi.org/10.1007/978-3-030-23876-6
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-23875-9Published: 30 August 2019
Softcover ISBN: 978-3-030-23878-0Published: 30 August 2020
eBook ISBN: 978-3-030-23876-6Published: 13 August 2019
Series ISSN: 2522-848X
Series E-ISSN: 2522-8498
Edition Number: 1
Number of Pages: XII, 355
Number of Illustrations: 32 b/w illustrations, 88 illustrations in colour
Topics: Signal, Image and Speech Processing, Probability and Statistics in Computer Science, Statistics and Computing/Statistics Programs, Probability Theory and Stochastic Processes