Structural EM for hierarchical latent class models
Nevin Lianwen Zhang · 2003
Hierarchical latent class (HLC) models are tree-structured Bayesian networks where leaf nodes are observed while internal nodes are not. This paper is concerned with the problem of learning HLC models from data. We apply the idea of structural EM to a hill-climbing algorithm for this task described in an accompanying paper (Zhang et al. 2003) and show empirically that the improved algorithm can learn HLC models that are large enough to be of practical interest.