Sentence examples for developed ensemble from inspiring English sources

The developed ensemble model can be potentially modified to model other types of MFCs or bioelectrochemical systems.

In this study, anomalies of terrestrial water storage (TWS) observed by the Gravity Recovery and Climate Experiment (GRACE) satellite mission were assimilated into the NASA Catchment land surface model in western and central Europe for a 7-year period, using a previously developed ensemble Kalman smoother.

CART allows for an exploration of response in a myriad of complex subpopulations, and more recently developed ensemble methods (such as Bayesian Additive Regression Trees) allow for more robust analyses through the combination of multiple CART analyses [ 37, 38].

The developed ensemble SVR model clearly gives better performances than the compared empirical correlations in estimating the three viscosity variables.

As the improved predictions of viscosity are always craved for, the developed ensemble support vector regression models could potentially replace the empirical correlation for viscosity prediction.

The frequency of human respiratory movement can be estimated based on the developed ensemble empirical mode decomposition (EEMD -based accumulation tEEMD -based caccumulation techniquenics effectively.

Those who developed ensemble-style taiko in Japan were men, and through the influence of Ondekoza, the "ideal taiko player" was epitomized in images of the masculine peasant class, particularly through the character Muhōmatsu in the 1958 film Rickshaw Man.

Most studies explored classification and regression trees, artificial neural networks or linear discriminant analysis, but only a few focused on more recently developed "ensemble-based" methods such as random forests or boosted regression trees [ 13, 16, 17].

Throughout the 1930s, steady engagements at the Savoy Ballroom in Harlem helped Webb maintain a stable roster of band personnel and develop ensemble discipline.

Further, in order to achieve better performances, we combine proposed methods and existing methods of the same type to develop ensemble models.

The motivation for developing ensemble of manifold learning components is that it can achieve higher accuracy and applicability than single component in machinery fault diagnosis.