Many recently developed supervised and unsupervised classification methods jointly rely on mixture and latent variable models. But due to the peculiarity of the supervised and unsupervised problems respectively, the role played by those two ingredients may be profoundly different. In this paper the various solutions are reviewed and compared and some new ideas are put forward.
A. Montanari (2007). Classification by mixture and latent variable models. MACERATA : EUM.
Classification by mixture and latent variable models
MONTANARI, ANGELA
2007
Abstract
Many recently developed supervised and unsupervised classification methods jointly rely on mixture and latent variable models. But due to the peculiarity of the supervised and unsupervised problems respectively, the role played by those two ingredients may be profoundly different. In this paper the various solutions are reviewed and compared and some new ideas are put forward.File in questo prodotto:
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