Principles of concurrent and distributed programming
Principles of concurrent and distributed programming
Learning in embedded systems
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Distributed Systems: Principles and Paradigms
Distributed Systems: Principles and Paradigms
Learning Structure from Data and Its Application to Ozone Prediction
Applied Intelligence
A Distributed Learning Algorithm for Bayesian Inference Networks
IEEE Transactions on Knowledge and Data Engineering
Learning probabilistic networks
The Knowledge Engineering Review
Collective Mining of Bayesian Networks from Distributed Heterogeneous Data
Knowledge and Information Systems
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Journal of Artificial Intelligence Research
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Learning Bayesian networks with local structure
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
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The implicit knowledge in the databases can be extracted of automatic form. One of the several approaches considered for this problem is the construction of graphical models that represent the relations between the variables and regularities in the data. In this work the problem is addressed by means of an algorithm of search and scoring. These kind of algorithms use a heuristic mechanism search and a function of score to guide themselves towards the best possible solution. The algorithm, which is implemented in the semifunctional language Lisp, is a searching mechanism of the structure of a bayesian network (BN) based on concurrent processes. Each process is assigned to a node of the BN and effects one of three possible operations between its node and some of the rest: to put, to take away or to invert an edge. The structure is constructed using the metric MDL (made up of three terms), whose calculation is made of distributed way, in this form the search is guided by selecting those operations between the nodes that minimize the MDL of the network. In this work are presented some results of the algorithm in terms of comparison of the structure of the obtained network with respect to its gold network.