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. Reports
advances on classic problems in mixture modeling such as parameter
estimation, model selection, and feature selection; Present
theoretical and practical developments in mixture-based modeling
and their importance in different applications; Discusses
perspectives and challenging future works related to mixture
modeling.
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