Learning with Mixtures of Trees

By Marina Meila, Michael I. Jordan. 2000

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Citations in the corpus

Model-Based Clustering, Discriminant Analysis, and Density Estimation (2002, methodology): “…models that have been applied in clustering and related contexts include mixtures of t distributions (Peel and McLachlan 2000), mixtures of trees (Meila 1999), mixtures of rst-order Markov chains (Cadez, Heckerman, Meek, Smyth, and White 2000), and mixtures of distributions for angular data…”
A new ensemble coevolution system for detecting HIV-1 protein coevolution (2015, methodology): “The parameters αk and Tk are thereafter optimized by the EM algorithm [49].”
Analyzing 3D Objects in Cluttered Images (2012, methodology): “Specifically, Meila and Jordan [20] describe an EM algorithm that iterates between inferring distributions over tree mixture assignments (the E-step) and estimating the tree structure (the M-step).”
A Probabilistic Framework for the Hierarchic Organisation and Classification of Document Collections (2002, methodology): “…a number of elegant methods have been suggested (Cooper and Herskovits, 1992), employing the Chew-Liu algorithm (maximum spanning branching) (Meilă and Jordan, 2000; Heckerman et al., 1995) or using decision graphs (Chickering et al., 1997), either these methods are applicable only for…”
Network Lasso: Clustering and Optimization in Large Graphs (2015, background): “Additionally, there has been much research on optimal decomposition and splitting methods for these types of problems [8, 19].”
Hierarchical latent class models for cluster analysis (2002, background): “Meila-Predoviciu (1999) studies how the so-called tree H models can be induced from data, where a tree H model is basically an LC model with each observed variable replaced by an undirected tree of observed variables.”