This book focuses on recent advances, approaches, theories and
applications related to mixture models. In particular, it presents
recent unsupervised and semi-supervised frameworks that consider mixture
models as their main tool. The chapters considers mixture models
involving several interesting and challenging problems such as
parameters estimation, model selection, feature selection, etc. The goal
of this book is to summarize the recent advances and modern approaches
related to these problems. Each contributor presents novel research, a
practical study, or novel applications based on mixture models, or a
survey of the literature.
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Reports advances on classic problems in mixture modeling such as
parameter estimation, model selection, and feature selection;
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Present theoretical and practical developments in mixture-based
modeling and their importance in different applications;
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Discusses perspectives and challenging future works related to mixture
modeling.