Sentence examples for based on mean square from inspiring English sources

An adaptive response prediction and model maturation mechanism is developed based on mean square error (MSE) to concurrently improve the accuracy and computational efficiency of the proposed approach.

In the designed algorithm, a multi-objective function based on Mean Square Error and Silhouette Index is employed to evaluate the two simultaneous distance functions namely-Classification measure and Anomaly detection measure.

In this paper we present EBaLM-OTR (error back propagation and Levenberg-Marquardt algorithms for over training resilience) technique, which is based on a previously discussed method of selecting neural network architecture that uses a separate validation set to evaluate different network architectures based on mean square error (MSE), and standard deviation of MSE.

We define an objective function based on mean square error (MSE) measure between estimated and target visual parameters.

In this part, we perform analyses for the proposed LTVFF-DRLS and LCTVFF-DRLS algorithms based on mean square performance and derive expressions for the steady-state MSD and EMSE, which are defined as begin{aligned} {MSD}_{k}^{ss}&={lim}_{itoinfty}Eleft[|widetilde{mathbf{w}}_{k,i}|^{2}right] {EMSE}_{k}^{ss}&={lim}_{itoinfty}Eleft[|mathbf{u}_{k,i}^widetilde{mathbf{w}}_{k,i-1}|^{2}right].

Similarly, Zou and Hastie [14] also showed that the performance of the elastic net in linear regression was superior to the Lasso based on mean square error (MSE) criteria, and Ogutu et al. reported similar accuracies for Lasso and elastic net methods for handling linear regression [34].

In this paper, we present a new formal test, which is based on mean squares, for analyzing three-level orthogonal saturated designs.

An algorithm based on mean squares successive difference test applied to near-infrared and principal component analysis scores was developed to monitor and determine the blending profile and to assess the end-point in the statistical stabile phase.

In SPLS, in addition to the number of components, the optimum number of variables was selected based on mean squared prediction error by 10-fold cross-validation (CV) of the training samples.

These fit indexes are both based on mean squares of the deviation between the data and the model, the only difference being that the infit statistics is a weighted estimate where persons close to the item location is given more weight than those at the tails of the distribution.

These datasets contained binary SDs of four model drugs with PVP based on root mean square error and mean absolute error for each quality attribute.