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In future work, more soft computing methods with statistical characteristic are used to the tribology analyses.
The techniques to combine conventional dimension reduction methods with statistical analysis methods can increase the efficiency of big data systems [8].
The Active Shape Models (ASM) [14] and Active Appearance Models (AAM) [15] are two popular appearance-based methods with statistical face models to prevent locating inappropriate feature points.
Comparing with the existing text mining systems (such as iHOP, PubGene, PIE) for discovering PPIs [3], [18], PPI Finder has several unique features by adopting the hybrid methods with statistical and computational linguistic theories.
We compare these methods with statistical learning methods Relief-F, Random Forests and Lasso.
This is, in effect, combining computational and semantic web methods with statistical methods, for a "big data" approach to available neuroimaging data.
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The proposed method makes use of the sample average approximation (SAA) method with statistical bounding techniques.
The results calculated by ray tracing method with statistical narrow band model, recently published, were taken as the benchmark.
It was found that the ICA-RBF model is the most accurate method with statistical values of RMSE = 1.16, R2 = 0.95 and AAPRE = 6.01%.
The generalization is based on a combination of the ESCD method with statistical linearization (SL) technique, modified adaptive time scheme (ATS), and time coordinate transformation (TCT).
A three-iteration method with statistical group response was then used to refine the standards.
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