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Due to the test costs and test organization, it is difficult to make a large number of tests, the precision of assessment can be improved by using unit information when estimating the reliability of electronic components. When the data are zero-failure, the precision can be improved obviously by using Bayes method with reducing the number of samplings. This paper presents that during the process of...
Prediction intervals that provide estimated values as well as the corresponding reliability are applied to nonlinear time series forecast. However, constructing reliable prediction intervals for noisy time series is still a challenge. In this paper, a bootstrapping reservoir computing network ensemble (BRCNE) is proposed and a simultaneous training method based on Bayesian linear regression is developed...
Random forest is an excellent ensemble learning method, which is composed of multiple decision trees grown on random input samples and splitting nodes on a random subset of features. Due to its good classification and generalization ability, random forest has achieved success in various domains. However, random forest will generate many noisy trees when it learns from the data set that has high dimension...
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