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- W2363368544 abstract "At present, the influence of noise can be reduced by simplifying the structure in the parameter learning of decomposable Markov network. But regulated structure will decrease the network reliability and the influence of noise can not be decreased or removed from root. In this paper, an effective method of smoothing noise is presented. Firstly, optimal neighborhood prediction is built based on the theory of decomposable Markov network. Then, a variable is taken as a basic unit and the noise can be smoothed by using prediction value to revise corresponding data. Finally, experimental results show that the reliability of learning parameters from smoothed data is greatly improved." @default.
- W2363368544 created "2016-06-24" @default.
- W2363368544 creator A5074405401 @default.
- W2363368544 date "2009-01-01" @default.
- W2363368544 modified "2023-09-25" @default.
- W2363368544 title "Noise smoothing in learning the parameters of decomposable Markov networks" @default.
- W2363368544 hasPublicationYear "2009" @default.
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