Fusion, propagation, and structuring in belief networks
Artificial Intelligence
A Bayesian method for constructing Bayesian belief networks from databases
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
From relational databases to belief networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Database systems: achievements and opportunities
Communications of the ACM
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Probabilistic similarity networks
Probabilistic similarity networks
Constructor: a system for the induction of probabilistic models
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Learning causal trees from dependence information
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Approximation algorithms for restricted Bayesian network structures
Information Processing Letters
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Bayesian learning for cardiac SPECT image interpretation
Artificial Intelligence in Medicine
Robotics and Computer-Integrated Manufacturing
An efficient node ordering method using the conditional frequency for the K2 algorithm
Pattern Recognition Letters
Hi-index | 0.00 |
This paper addresses learning stochastic rules especially on an inter-attribute relation based on a Minimum Description Length (MDL) principle with a finite number of examples, assuming an application to the design of intelligent relational database systems. The stochastic rule in this paper consists of a model giving the structure like the dependencies of a Bayesian Belief Network (BBN) and. some stochastic parameters each indicating a conditional probability of an attribute value given the state determined by the other attributes' values in the same record, Especially, we propose the extended version of the algorithm of Chow and Liu in that our learning algorithm selects the model in the range where the dependencies among the attributes are represented by some general plural number of trees.