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HMM 2021 : Hidden Markov Models and Applications

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When Jul 30, 2021 - Jul 30, 2021
Where Montreal
Abstract Registration Due Jan 15, 2021
Submission Deadline May 15, 2021
Notification Due Jun 15, 2021
Final Version Due Jul 30, 2021
 

Call For Papers

Dear Colleagues,



We would like to invite you to contribute a chapter for our upcoming volume entitled “Hidden Markov 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 2021/early 2022 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, Hidden Markov models (HMMs) have received a lot of attention thanks to their flexibility and ease of use. HMMs have been applied in many areas such as communication engineering, smart buildings, energy, computer vision, pattern recognition, data mining, signal processing, security, quality control, bioinformatics, financial engineering, remote sensing, etc.

This book will focus on recent advances, approaches, theories and applications related HMMs. In particular, it aims to present recent inference frameworks and applications that consider HMMs. The consideration of HMMs for a specific task or application involves several interesting and challenging problems (e.g. estimation, 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)

• Hidden Markov models
• Infinite Hidden Markov models
• Bayesian/variational learning
• Nonparametric Bayesian approaches
• Model selection
• Feature selection
• High-dimensional data
• Outliers detection
• Active learning
• Big Data
• Transfer learning
• 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 hidden Markov 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 January 15, 2021

Notification of initial editorial decisions January 30, 2021

Submission of full-length chapters May 15, 2021

Notification of final editorial decisions June 15, 2021

Submission of revised chapters July 30, 2021



All submissions should be submitted by email to the editors:

Prof. Nizar Bouguila, Concordia University, Canada (nizar.bouguila@concordia.ca)
Prof. Wentao Fan, Huaqiao University, China (fwt@hqu.edu.cn)
Dr. Manar Amayri, Grenoble Institute of Technology, France (Manar.amayri@grenoble-inp.fr)

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

https://www.springer.com/gp/authors-editors/book-authors-editors/resources-guidelines/book-manuscript-guidelines




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



Sincerely,



Nizar Bouguila, Wentao Fan, and Manar Amayri

Editors

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