Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
Extending learning to multiple agents: issues and a model for multi-agent machine learning (MA-ML)
EWSL-91 Proceedings of the European working session on learning on Machine learning
Principles of distributed database systems (2nd ed.)
Principles of distributed database systems (2nd ed.)
Advanced Database Technology and Design
Advanced Database Technology and Design
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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Data-mining is concerned with extracting knowledge from databases (in this case we deal with distributed ones) using machine-learning techniques. Traditionally, data-mining systems are designed to work on a single data set. With the large number of distributed databases dispersed in WANS with geographically spread locations, or in Internet, it is necessary to adopt new techniques to improve the mining results. The development of Bayesian belief networks and associated algorithms made possible that probabilistic reasoning becomes a real option for a large variety of Artificial Intelligence applications. In this paper we present a methodology for machine learning using Bayesian belief network with practical exemplification for predicting arteriosclerosis cardiovascular disease, with data located on different sites across a computer network.