Elements of information theory
Elements of information theory
The EM algorithm for graphical association models with missing data
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Efficient Approximations for the MarginalLikelihood of Bayesian Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
Algorithms for Model-Based Gaussian Hierarchical Clustering
SIAM Journal on Scientific Computing
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Learning Belief Networks in the Presence of Missing Values and Hidden Variables
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Learning the Dimensionality of Hidden Variables
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Learning with mixtures of trees
Learning with mixtures of trees
Asymptotic model selection for directed networks with hidden variables*
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
"Ideal Parent" structure learning for continuous variable networks
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Learning Hidden Variable Networks: The Information Bottleneck Approach
The Journal of Machine Learning Research
Classification using Hierarchical Naïve Bayes models
Machine Learning
Artificial Intelligence in Medicine
A latent class modeling approach to detect network intrusion
Computer Communications
A hierarchical mixture model for clustering three-way data sets
Computational Statistics & Data Analysis
Latent tree models and diagnosis in traditional Chinese medicine
Artificial Intelligence in Medicine
Agglomerative independent variable group analysis
Neurocomputing
Discovering Latent Structures: Experience with the CoIL Challenge 2000 Data Set
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
Hierarchical Latent Class Models and Statistical Foundation for Traditional Chinese Medicine
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Latent tree models and approximate inference in Bayesian networks
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Effective dimensions of hierarchical latent class models
Journal of Artificial Intelligence Research
Latent tree models and approximate inference in Bayesian networks
Journal of Artificial Intelligence Research
A hierarchical Naïve Bayes model for approximate identity matching
Decision Support Systems
Learning Latent Tree Graphical Models
The Journal of Machine Learning Research
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Model-based multidimensional clustering of categorical data
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
Discovery of regularities in the use of herbs in traditional chinese medicine prescriptions
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Model-based clustering of high-dimensional data: Variable selection versus facet determination
International Journal of Approximate Reasoning
LTC: A latent tree approach to classification
International Journal of Approximate Reasoning
A survey on latent tree models and applications
Journal of Artificial Intelligence Research
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Latent class models are used for cluster analysis of categorical data. Underlying such a model is the assumption that the observed variables are mutually independent given the class variable. A serious problem with the use of latent class models, known as local dependence, is that this assumption is often untrue. In this paper we propose hierarchical latent class models as a framework where the local dependence problem can be addressed in a principled manner. We develop a search-based algorithm for learning hierarchical latent class models from data. The algorithm is evaluated using both synthetic and real-world data.