Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Models of incremental concept formation
Artificial Intelligence
An architecture for probabilistic concept-based information retrieval
SIGIR '90 Proceedings of the 13th annual international ACM SIGIR conference on Research and development in information retrieval
Experiments with Incremental Concept Formation: UNIMEM
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Node aggregation for distributed inference in Bayesian networks
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Decomposition of structural learning about directed acyclic graphs
Artificial Intelligence
Comparing Bayesian network classifiers
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
An algorithm for the construction of Bayesian network structures from data
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
A construction of Bayesian networks from databases based on an MDL principle
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
A Bayesian method for constructing Bayesian belief networks from databases
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
Probability estimation in face of irrelevant information
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
Expert Systems with Applications: An International Journal
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Adaptive thresholding in structure learning of a Bayesian network
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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The probabilistic network technology is a knowledge-based technique which focuses on reasoning under uncertainty. Because of its well defined semantics and solid theoretical foundations, the technology is finding increasing application in fields such as medical diagnosis, machine vision, military situation assessment, petroleum exploration, and information retrieval. However, like other knowledge-based techniques, acquiring the qualitative and quantitative information needed to build these networks can be highly labor-intensive. CONSTRUCTQR integrates techniques and concepts from probabilistic networks, artificial intelligence, and statistics in order to induce Markov networks (i.e., undirected probabilistic networks). The resulting networks are useful both qualitatively for concept organization and quantitatively for the assessment of new data. The primary goal of CONSTRUCTOR is to find qualitative structure from data. CONSTRUCTOR finds structure by first, modeling each feature in a data set as a node in a Markov network and secondly, by finding the neighbors of each node in the network. In Markov networks, the neighbors of a node have the property of being the smallest set of nodes which "shield" the node from being affected by other nodes in the graph. This property is used in a heuristic search to identify each node's neighbors. The traditional χ2 test for independence is used to test if a set of nodes "shield" another node. Cross-validation is used to estimate the quality of alternative structures.