The evaluation criterion is the root mean squared error (RMSE) between predicted ratings and true ones.

评判标准是预测打分和真实值之间的均方误差(RMSE)。

The process of which is to solve the integer programming problem with the objective function that RMSE is minimal.

选择最优参数的过程即是对以均方根误差最小为目标的整数规划问题的求解过程。

Examples in Test1 are used for calculating the scores shown on the Leaderboard: RMSE for Track1 and Error rate for Track2.

test1里的例子是用来计算领先选手排行榜(Leaderboard)上的分数：track1的RMSE和Track2的错误率(Error rate)。

The results show that the root mean square error(RMSE) of the control points obtained by the workflow is lower than 0.5 pixels.

结果表明，使用该方法选取的控制点均方根误差（RMSE）可以控制在0.5个像素以内。

The results of the test data indicate that the prediction system is reliable and the root of mean square error (RMSE) is about 15%.

对测试数据的预测 结果表明，该预测系统能够可靠工作，预测结果的均方根 误差在 15%左右。

The resulted showed that KBSI had the minimal MAE and RMSE. The spatial distributions of monthly mean temperature were also reasonable.

气温的空间分布趋势也更加合理，更好的体现出气温分布的空间异质性。

Testing of the monitoring models with independent dataset indicated that the predictive precision (R2 ) was 0.6824, and RMSE was 0.4052.

经不同年际独立试验数据的检验，叶片碳氮比监测模型的预测精确R2为0.6824，根均方差（RMSE）为0.4052。

Results show that the RBFNN is obviously superior to the traditional linear model, and its MAE (mean absolute error) and RMSE (root mean square error) are 41.8 and 55.7, respectively.

结果显示，该模型预测效果明显优于传统的线性自回归预测模型，各月平均的平均绝对误差（MAE）和均方误差（RMSE）达到41.8和55.7。

The popular fidelity measurement method based on root mean squared error (RMSE) is unable to completely reflect the details of the sensitive information of compressed grayscale images.

常用的基于均方根误差(RMSE)图像保真度准则不能准确地放映一些灰度图像主要敏感细节。

Forecast results show that Bayesian neural network MAPE and RMSE are less than artificial neural network, Bayesian neural network with better performance, it can be applied to predict the actual work.

预测结果表明，贝叶斯神经网络的MAPE和RMSE均小于人工神经网络，贝叶斯神经网络具有更好的性能，它可利用于实际预测工作中。