A Local Machine Learning Task in Algebraical Bayesian Networks: a Probabilistic- Logic Approach
Abstract
One of the problems that slow down intelligent information systems development and industry- wide spread is so-called knowledge bottleneck. Machine learning for various uncertain knowledge representations and models used in intelligent systems is a promising way to cope with the bottleneck. Algebraical Bayesian networks are a probabilistic graphical model that allows for representing and processing interval estimates of probabilities. The paper goal is to describe the machine learning task in regard to a knowledge pattern of an algebraical Bayesian network as well as to present a few ways to solve the task and to outline obstacles related to those ways.References
Published
2008-04-01
How to Cite
Tulupyev,. (2008). A Local Machine Learning Task in Algebraical Bayesian Networks: a Probabilistic- Logic Approach. SPIIRAS Proceedings, (7), 11-25. https://doi.org/10.15622/sp.7.1
Section
Articles
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