[visionlist] Call for book chapters: Mixture Models and Applications

Nizar Bouguila Nizar.Bouguila at USherbrooke.ca
Sat Dec 8 19:09:42 -05 2018

Dear Colleagues,

We would like to invite you to contribute a chapter for our upcoming volume entitled Mixture Models and Applications to be published in the book series Unsupervised and Semi-Supervised Learning,  Springer, the largest global scientific, technical, and medical ebook publisher. The volume will be available both in print and in ebook format by late 2019/early 2020 on SpringerLink, one of the leading science portals that includes more than 10 million documents, an ebook collection with more than 174,000 titles, and journal archives digitized back to the first issues in the 1840s.

Below is a short description of the volume:

During the last years, mixture models have received a lot of attention thanks to their flexibility and ease of use. Mixture models have been applied in many areas such as computer vision, pattern recognition, data mining, security, quality control, bioinformatics, financial engineering, remote sensing, etc. Mixture models are known for their ability to offer a formal approach to unsupervised and semi-supervised learning.

This book will focus on recent advances, approaches, theories and applications related to mixture models. In particular, it aims to present recent unsupervised and semi-supervised frameworks that consider mixture models as their main tool. The consideration of mixture models involves several interesting and challenging problems such as parameters estimation, model selection, feature selection, etc. The goal of this volume is to summarize the recent advances and modern approaches related to these problems. Topics of interest include: (but not limited to)

  •   Finite mixture models
  •   Infinite mixture models
  •   Bayesian/variational learning
  •   Nonparametric Bayesian approaches
  •   Model selection
  •   Feature selection
  •   Subspace mixture models
  •   Outliers detection
  •   High-dimensional data
  •   Big Data
  •   Deep mixture models
  •   Unsupervised learning
  •   Semi-supervised learning
  •   Online learning
  •   Time series
  •   Applications
Each contributed chapter is expected to present a novel research, a practical study or novel applications based on mixture models, or a survey of the literature.

Note that there will be absolutely no publication fees for accepted chapters. Note that a well-written book chapter has the potential to receive hundreds of citations because chapters are often longer and more detailed than other types of articles and thus more researchers can find material that is of interest to them in a chapter.

Important Dates

Submission of abstracts                      December 15, 2018

Notification of initial editorial decisions   December 30, 2018

Submission of full-length chapters           March 15, 2019

Notification of final editorial decisions    April 1, 2019

Submission of revised chapters               May 1, 2019

All submissions should be submitted by email to the editors:

Prof. Nizar Bouguila, Concordia University, Canada (nizar.bouguila at concordia.ca)
Dr. Wentao Fan, Huaqiao University, China  (fwt at hqu.edu.cn)

Original artwork and a signed copyright release form will be required for all
accepted chapters. For author instructions, please visit:


Feel free to contact us via email regarding your chapter ideas.


Nizar Bouguila and Wentao Fan


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