Operations and evaluation measures for learning possibilistic graphical models

DOI10.1016/S0004-3702(03)00024-9zbMath1082.68600OpenAlexW2079927294MaRDI QIDQ814501

Rudolf Kruse, Christian Borgelt

Publication date: 7 February 2006

Published in: Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0004-3702(03)00024-9

zbMATH Keywords

Graphical modelsLearning from dataPossibilistic networksEvaluation measures

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

On the Analysis of Probability-Possibility Transformations: Changing Operations and Graphical Models

Uses Software

• C4.5
• PULCinella

Cites Work

• A Mathematical Theory of Communication
• On the shortest spanning subtree of a graph and the traveling salesman problem
• Where do we stand on measures of uncertainty, ambiguity, fuzziness, and the like?
• Structure identification in relational data
• A Bayesian method for the induction of probabilistic networks from data
• The context model: An integrating view of vagueness and uncertainty
• Learning Bayesian networks: The combination of knowledge and statistical data
• An Introduction to Spatial Point Processes and Markov Random Fields
• MEASURES OF UNCERTAINTY AND INFORMATION BASED ON POSSIBILITY DISTRIBUTIONS
• Conjugate Gradient Acceleration of the EM Algorithm
• Approximating discrete probability distributions with dependence trees
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Operations and evaluation measures for learning possibilistic graphical models