Mixture Model-Based Classification

Paul D McNicholas

ISBN 10: 1482225662 / ISBN 13: 9781482225662

Editore: CRC Press Aug 2016, 2016

Riguardo questo articolo

Neuware - This work addresses classification using mixture models broadly. Unlike traditional treatments of the subject that heavily focus on unsupervised approaches, this book gives attention to unsupervised, semi-supervised, and supervised classification paradigms. Case studies illustrate both non-Gaussian and Gaussian approaches to model selection.

Codice articolo 9781482225662

Dati bibliografici

• Titolo: Mixture Model-Based Classification
• Editore: CRC Press Aug 2016
• Data di pubblicazione: 2016
• Lingua: Inglese
• ISBN 10: 1482225662
• ISBN 13: 9781482225662
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 234 L x 157 A x 20 P (millimetri) 234 millimetri di larghezza per 157 millimetri di altezza per 20 millimetri di profondità
• Peso: 476 grammi

Descrizione articolo

Riassunto

"This is a great overview of the field of model-based clustering and classification by one of its leading developers. McNicholas provides a resource that I am certain will be used by researchers in statistics and related disciplines for quite some time. The discussion of mixtures with heavy tails and asymmetric distributions will place this text as the authoritative, modern reference in the mixture modeling literature." (Douglas Steinley, University of Missouri)

Mixture Model-Based Classification is the first monograph devoted to mixture model-based approaches to clustering and classification. This is both a book for established researchers and newcomers to the field. A history of mixture models as a tool for classification is provided and Gaussian mixtures are considered extensively, including mixtures of factor analyzers and other approaches for high-dimensional data. Non-Gaussian mixtures are considered, from mixtures with components that parameterize skewness and/or concentration, right up to mixtures of multiple scaled distributions. Several other important topics are considered, including mixture approaches for clustering and classification of longitudinal data as well as discussion about how to define a cluster

Paul D. McNicholas is the Canada Research Chair in Computational Statistics at McMaster University, where he is a Professor in the Department of Mathematics and Statistics. His research focuses on the use of mixture model-based approaches for classification, with particular attention to clustering applications, and he has published extensively within the field. He is an associate editor for several journals and has served as a guest editor for a number of special issues on mixture models.

Informazioni sull?autore

Paul D. McNicholas is the Canada Research Chair in Computational Statistics at McMaster University, where he is a Professor in the Department of Mathematics and Statistics. His research focuses on the use of mixture model-based approaches for classification, with particular attention to clustering applications, and he has published extensively within the field. He is an associate editor for several journals and has served as a guest editor for a number of special issues on mixture models.