• Produktbild: Mixture Models and Applications
  • Produktbild: Mixture Models and Applications

Mixture Models and Applications

97,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Lieferung nach Hause

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

30.08.2020

Herausgeber

Nizar Bouguila + weitere

Verlag

Springer

Seitenzahl

355

Maße (L/B/H)

23,5/15,5/2 cm

Gewicht

557 g

Auflage

1st ed. 2020

Sprache

Englisch

ISBN

978-3-030-23878-0

Beschreibung

Rezension

“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)

Portrait

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. 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.

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

30.08.2020

Herausgeber

Verlag

Springer

Seitenzahl

355

Maße (L/B/H)

23,5/15,5/2 cm

Gewicht

557 g

Auflage

1st ed. 2020

Sprache

Englisch

ISBN

978-3-030-23878-0

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

Kundinnen und Kunden meinen

0 Bewertungen

Informationen zu Bewertungen

Zur Abgabe einer Bewertung ist eine Anmeldung im Konto notwendig. Die Authentizität der Bewertungen wird von uns nicht überprüft. Wir behalten uns vor, Bewertungstexte, die unseren Richtlinien widersprechen, entsprechend zu kürzen oder zu löschen.

Die Bewertungen sind nach Format, Anzahl Sterne und Datum sortiert.

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kund*innen durch Ihre Meinung

Kundinnen und Kunden meinen

0 Bewertungen filtern

  • Produktbild: Mixture Models and Applications
  • Produktbild: Mixture Models and Applications
  • 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.