Effects of additional data on Bayesian clustering

Hierarchical probabilistic models, such as mixture models, are used for cluster analysis. These models have two types of variables: observable and latent. In cluster analysis, the latent variable is estimated, and it is expected that additional information will improve the accuracy of the estimation...

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Veröffentlicht in:	Neural networks 2017-10, Vol.94, p.86-95
1. Verfasser:	Yamazaki, Keisuke
Format:	Artikel
Sprache:	eng
Schlagworte:	Bayes Theorem Cluster Analysis Hierarchical parametric models Latent variable estimation Models, Statistical Semi-supervised learning Supervised Machine Learning Unsupervised learning
Online-Zugang:	Volltext
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