Sentence examples for model estimation performance from inspiring English sources
The authors of this paper have demonstrated that initial guessing of parameter can have significant effect on overall model estimation performance.
This suggests that initial guessing of parameters can affect a model estimation performance to a high extent in terms of iteration numbers, convergence time and so on.
The other objectives of this research are (a) to identify the factors affecting the model estimation performance and (b) to suggest potential research topics related to custom-built modeling.
(a) Different mathematical formulations for stopping criterion may be tried and different level of stopping accuracy such as ( left[ {frac{1}{k}sumnolimits_{k = 1}^{k} {(beta_{t + 1,k} - beta_{t,k} )^{2} } } right]^{1/2} < 10^{ - 6} ), 10−7 can be adopted to make a significant difference in model estimation performance.
The weighted 4-quadrant model improved estimation performance and reproduced the directionality of psychophysical results.
We also propose a performance estimation model that estimates performance for Hadoop K-means iterations by modeling different processor micro-architecture parameters.
The simulation study has shown that the LCAR model exhibits generally superior estimation performance for fixed effects compared with both the commonly used BYM model and the recent innovations by Lee and Mitchell (2013) and Hughes and Haran (2013).
A number of experiments are conducted and the results show that local modeling can enhance the estimation performance of a forecasting method for time series prediction.
We conclude that the model with a step function is preferred over its competitor due to better estimation performance and model flexibility.
On the other hand, the Fuzzy Logic approach leads to a Gaussian variogram model and significantly improves the estimation performance.
Figure 8 Authentication performance using model estimation with the EM algorithm ( N = 2,000, N o = 2,000, σ = 52, m b = 50, and m w = 150 ).
