Paul D. McNicholas

Mixture Model-Based Classification (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 236
• Erscheinungstermin: 4. Oktober 2016
• Englisch
• ISBN-13: 9781482225679
• Artikelnr.: 46757646

Autorenporträt

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.

Inhaltsangabe

Mixture Model-Based Classification

Rezensionen

"This Monograph, "Mixture Model-Based Classification" is an excellent book, highly relevant to every statistician working with classification problems."
~International Society for Clinical Biostatistics

"This monograph is an extensive introduction of mixture models with applications in classification and clustering. . . The author did good work by organizing the materials in a very natural way as well as presenting methods and algorithms in great detail. Moreover, many case studies help the reader understand and appreciate the methodologies presented."
~Journal of the American Statistical Association

"I would recommend this book to anyone interested in learning about application of mixture models to classification problems."
~The International Biometric Society