Using Unlabelled Data to Update Classification Rules with Applications in Food Authenticity Studies
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Publication:5757808
DOI10.1111/j.1467-9876.2005.00526.xzbMath1490.62155OpenAlexW2097421680MaRDI QIDQ5757808
Thomas Brendan Murphy, Nema Dean, Gerard Downey
Publication date: 7 September 2007
Published in: Journal of the Royal Statistical Society Series C: Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1111/j.1467-9876.2005.00526.x
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics in engineering and industry; control charts (62P30)
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Uses Software
Cites Work
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- The distribution of the likelihood ratio for mixtures of densities from the one-parameter exponential family
- Enhanced model-based clustering, density estimation, and discriminant analysis software:\newline MCLUST
- MCLUST: Software for model-based cluster analysis
- A classification EM algorithm for clustering and two stochastic versions
- A theory for multiresolution signal decomposition: the wavelet representation
- Ten Lectures on Wavelets
- How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
- Model-Based Clustering, Discriminant Analysis, and Density Estimation
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