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The linear minimum mean-square error (LMMSE) estimation has been shown to provide a good tradeoff between the computational requirement and estimation accuracy in nonlinear point estimation. However, the best estimator within the linear class may not be adequate to provide acceptable accuracy when dealing with a highly nonlinear problem. A generalized LMMSE (GLMMSE) estimation framework searches for...
The class label of each feature vector in the dataset is respectively added in the corresponding feature vector as a feature value, which build a new vector called altered feature vector, all of which compose the altered dataset. It is demostrated that an SVM based on the altered dataset has advantages such as high generilization performance and little structure risk, compared with an SVM based on...
This study aimed to investigate the clinical value of DSCT in the preoperational assessment of the cardiovascular malformation in patients with the complex congenital heart disease of diminished pulmonary blood flow in China. 130 patients' scheduled for operation because of suspected or definited complex congenital heart disease with diminished pulmonary blood flow were examined by DSCT and echo cardiography...
This paper focuses on distributed implementation of active learning with a limited number of queries. In the prognostics and health management domain, the cost to obtain a training sample can be fairly high, especially when studying the aging process for remaining useful life prediction of a mission critical component. Active learning with limited resource is formulated as a reinforcement learning...
In order to improve the prediction accuracy, learning vector quantity (LVQ) was applied to construct solar proton event prediction model. LVQ is new type of neutral network based on competitive learning rule, which takes a supervised learning model. The structure of LVQ is a two layers neutral network. The input unit is the predictors which are some active region parameters correlated to proton event...
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