Semi-supervised Gaussian mixture modelling with a missing-data mechanism in \textsf{R}
type: Item
instance of: scholarly article
title: Semi-supervised Gaussian mixture modelling with a missing-data mechanism in \textsf{R}
author: Ziyang Lyu
author: Daniel Ahfock
author: Ryan C. Thompson
author: Geoffrey J. McLachlan
published in: Australian \& New Zealand Journal of Statistics
publication date: 2024-07-30T00:00:00Z
Mathematics Subject Classification ID: 62Fxx
Mathematics Subject Classification ID: 62Hxx
Mathematics Subject Classification ID: 68Txx
zbMATH DE Number: 7889429
zbMATH Keywords: Bayes' rule
zbMATH Keywords: entropy
zbMATH Keywords: mixture model
zbMATH Keywords: partially classified sample
zbMATH Keywords: semi-supervised learning
MaRDI profile type: Publication
cites work: An apparent paradox: a classifier based on a partially classified sample may have smaller expected error rate than that if the sample were completely classified
cites work: Q5715698
cites work: Learning from partially supervised data using mixture models and belief functions
cites work: Q4139463
cites work: The efficiency of a linear discriminant function based on unclassified initial samples
cites work: Texture classification and segmentation using wavelet packet frame and Gaussian mixture model
cites work: Q3093181
cites work: Finite mixture models
cites work: Iterative Reclassification Procedure for Constructing an Asymptotically Optimal Rule of Allocation in Discriminant Analysis
cites work: Estimating the Linear Discriminant Function from Initial Samples Containing a Small Number of Unclassified Observations
cites work: The EM Algorithm and Extensions, 2E
cites work: Clarifying missing at random and related definitions, and implications when coupled with exchangeability: Table 1.
cites work: Maximum likelihood estimation via the ECM algorithm: A general framework
cites work: Inference and missing data
DOI: 10.1111/ANZS.12413
description: scientific article; zbMATH DE number 7889429