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Unsupervised and Semi-Supervised Learning

Mixture Models and Applications

Editors: Bouguila, Nizar, Fan, Wentao (Eds.)

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  • 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
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eBook $84.99
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  • ISBN 978-3-030-23876-6
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  • Immediate eBook download after purchase
Hardcover $129.99
price for USA in USD
  • ISBN 978-3-030-23875-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

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.

About the authors

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 d’excellence de l’association 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. He is 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.

Table of contents (14 chapters)

Table of contents (14 chapters)
  • A Gaussian Mixture Model Approach to Classifying Response Types

    Pages 3-22

    Parsons, Owen E.

  • Interactive Generation of Calligraphic Trajectories from Gaussian Mixtures

    Pages 23-38

    Berio, Daniel (et al.)

  • Mixture Models for the Analysis, Edition, and Synthesis of Continuous Time Series

    Pages 39-57

    Calinon, Sylvain

  • Multivariate Bounded Asymmetric Gaussian Mixture Model

    Pages 61-80

    Azam, Muhammad (et al.)

  • Online Recognition via a Finite Mixture of Multivariate Generalized Gaussian Distributions

    Pages 81-106

    Najar, Fatma (et al.)

Buy this book

eBook $84.99
price for USA in USD (gross)
  • ISBN 978-3-030-23876-6
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $129.99
price for USA in USD
  • ISBN 978-3-030-23875-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Mixture Models and Applications
Editors
  • Nizar Bouguila
  • Wentao Fan
Series Title
Unsupervised and Semi-Supervised Learning
Copyright
2020
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-23876-6
DOI
10.1007/978-3-030-23876-6
Hardcover ISBN
978-3-030-23875-9
Series ISSN
2522-848X
Edition Number
1
Number of Pages
XII, 355
Number of Illustrations
32 b/w illustrations, 88 illustrations in colour
Topics