Sentence examples for degree estimation in from inspiring English sources

This section presents a method for error degree estimation in numerical weather prediction using MKDA-based ordinal regression.

Therefore, the example-based approach from many training examples is also useful for prediction error degree estimation in numerical weather prediction.

In this paper, we present a new method for error degree estimation in numerical weather prediction via multiple kernel discriminant analysis (MKDA -based ordinal regression.

From the above discussion, we focus on the KDA-based ordinal regression extended to a multiple kernel version for error degree estimation in numerical weather prediction in this paper.

Since prediction errors tend to be propagated to neighboring areas according to atmospheric movements, this characteristic should be considered in prediction error degree estimation.

Then, by using the features calculated from the previously known prediction errors caused in the target element F0 and some other elements (corresponding to F1 and F2), unknown error degree estimation of each target element shown in the left side of Table 1 is performed.

In this paper, we try to perform accurate prediction error degree estimation based on ordinal regression.

Incorporated with the principles of the improved semi-supervised learning, the opportunities of applying and updating the surrogate model are determined by its confidence degree in estimation, and the informative individuals reevaluated by the user are selected according to the concept of learning from mistakes.

Figure 4 Root-mean-square error (RMSE) of angle estimation in degrees versus signal to noise ratio (SNR) in dB for ( θ 1,2 = 75°, 45°).

Figure 5 shows the RMSE of the angle estimation in degrees for the proposed EDBA-2D method compared with the ESPRIT method for different SNRs.

Figure 5 Root-mean-square error (RMSE) of angle estimation in degrees versus signal to noise ratio (SNR) in dB for ( θ 1,2 = 75°, 45°) and ( ϕ 1,2 = 80°, 55°).