Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Learning Belief Networks in the Presence of Missing Values and Hidden Variables
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Hierarchical latent class models for cluster analysis
Eighteenth national conference on Artificial intelligence
Learning equivalence classes of bayesian-network structures
The Journal of Machine Learning Research
Hierarchical Latent Class Models for Cluster Analysis
The Journal of Machine Learning Research
Efficient Learning of Hierarchical Latent Class Models
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
The role of operation granularity in search-based learning of latent tree models
JSAI-isAI'10 Proceedings of the 2010 international conference on New Frontiers in Artificial Intelligence
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We present a case study to demonstrate the possibility of discovering complex and interesting latent structures using hierarchical latent class (HLC) models. A similar effort was made earlier ?, but that study involved only small applications with 4 or 5 observed variables. Due to recent progress in algorithm research, it is now possible to learn HLC models with dozens of observed variables. We have successfully analyzed a version the CoIL Challenge 2000 data set that consists of 42 observed variable. The model obtained consists of 22 latent variables, and its structure is intuitively appealing.