Artificial Intelligence in Medicine
Latent tree models and diagnosis in traditional Chinese medicine
Artificial Intelligence in Medicine
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
Trigger to Switch Individual's Interest Toward Unconscious Preference
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Reflective visualisation and verbalisation of unconscious preference
International Journal of Advanced Intelligence Paradigms
Using physicochemical properties of amino acids to induce graphical models of residue couplings
Proceedings of the Tenth International Workshop on Data Mining in Bioinformatics
Learning Latent Tree Graphical Models
The Journal of Machine Learning Research
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
Model-based clustering of high-dimensional data: Variable selection versus facet determination
International Journal of Approximate Reasoning
A survey on latent tree models and applications
Journal of Artificial Intelligence Research
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Hierarchical latent class (HLC) models are tree-structured Bayesian networks where leaf nodes are observed while internal nodes are hidden. In earlier work, we have demonstrated in principle the possibility of reconstructing HLC models from data. In this paper, we address the scalability issue and develop a search-based algorithm that can efficiently learn high-quality HLC models for realistic domains. There are three technical contributions: (1) the identification of a set of search operators; (2) the use of improvement in BIC score per unit of increase in model complexity, rather than BIC score itself, for model selection; and (3) the adaptation of structural EM for situations where candidate models contain different variables than the current model. The algorithm was tested on the COIL Challenge 2000 data set and an interesting model was found.