On fast supervised learning for normal mixture models with missing information
碩士 === 東海大學 === 統計學系 === 93 === It is an important research issue to deal with mixture models when missing values occur in the data. In this paper, computational strategies using auxiliary indicator matrices are introduced for handling mixtures of multivariate normal distributions in a more efficien...
Main Authors: | Hsiu J. Ho, 何秀榮 |
---|---|
Other Authors: | Tsung I. Lin |
Format: | Others |
Language: | en_US |
Published: |
2005
|
Online Access: | http://ndltd.ncl.edu.tw/handle/14446657169844690511 |
Similar Items
-
Efficient computational schemes for supervised learning ofmultivariate t mixture models with missing information
by: Chia-Yu Chang, et al.
Published: (2006) -
Automated learning of mixtures of factor analyzers with missing information
by: Ying-Ting Lin, et al.
Published: (2018) -
Parsimonious Gaussian Mixture Modelling With Missing Information
by: Chia-Hua Chen, et al.
Published: (2011) -
Model-based clustering via mixture of skew-t distribution with missing information
by: Chia-Hui Hsu, et al.
Published: (2014) -
Mixture-Model-Based Graph for Privacy-Preserving Semi-Supervised Learning
by: Zhi Li, et al.
Published: (2020-01-01)