Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
PULCinella: a general tool for propagating uncertainty in valuation networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Probabilistic similarity networks
Probabilistic similarity networks
Fuzzy logic for the management of uncertainty
Fuzzy logic for the management of uncertainty
Valuation-based systems: a framework for managing uncertainty in expert systems
Fuzzy logic for the management of uncertainty
Fuzzy information engineering: a guided tour of applications
Fuzzy information engineering: a guided tour of applications
Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence
Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Graphical Models: Methods for Data Analysis and Mining
Graphical Models: Methods for Data Analysis and Mining
Foundations of Fuzzy Systems
HUGIN: a shell for building Bayesian belief universes for expert systems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Update rules for parameter estimation in Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
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Data Mining, or Knowledge Discovery in Databases, is a fairly young research area that has emerged as a reply to the flood of data we are faced with nowadays. It tries to meet the challenge to develop methods that can help human beings to discover useful patterns in their data. One of these techniques -- and definitely one of the most important, because it can be used for such frequent data mining tasks like classifier construction and dependence analysis -- is learning graphical models from datasets of sample cases. In this paper we review the ideas underlying graphical models, with a special emphasis on the less well known possibilistic networks. We discuss the main principles of learning graphical models from data and consider briefly some algorithms that have been proposed for this task as well as data preprocessing methods and evaluation measures.