Mean error (Math. Phys.), the average error of a number of observations found by taking the mean value of the positive and negative errors without regard to sign. – Mean-square error, or Error of the mean square (Math. Phys.), the error the square of which is the mean of th...
The EPM training process involves the use of the Adam optimizer with a learning rate of 0.001 and the mean square error as the loss metric. The input is a four-dimensional tensor of size [c, t, h, w] (6, 24, 32, 32), where c represents the number of input channels, t represents...
The root mean squared sensitivity of the observations is σI = 47 and σQ ≅ σU = 9 μJy beam−1. In Fig. 1 we present image plane maps of the total intensity I, Stokes Q and U, and polarized intensity (PI). The polarization angle χ is rotated by 90° ...
Table 6: Performance comparison (mean square error) on benchmark datasets. Table 7: Rationales (words in boldface) from user and item reviews extracted by R3 for predicting the targeted review and rating. The targeted review is:foo...
Ecuador has participated in the world’s major tourism expo and has formulated magnificent strategies for the extremely diversified tourism destinations in our country. Ecuador covers an area of about 280,000 square kilometers, but it brings together four geographic regions: the Pacific coast, the ...
__name__ == "square" # - brackets denote a multiple-expression lambda body # (if you want to have one expression that is a literal list, # double the brackets: `lambda x: [[5 * x]]`) # - local[name := value] makes an expression-local variable g = lambda x: [local[y :=...
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Pb dates and define a discordia with an upper intercept at 2819 ± 8 Ma (mean square weighted deviation, MSWD = 1.4; all ages reported at 95% confidence level) anchored by five concordant analyses (Fig. 6a and Tab. S10). The zoning, chemistry and consistent dates of the ...
(node size). We used the randomForest package in R101to select parameter values that minimized the mean square error of predictions when compared with the validation set. For all three models, we found the optimal parameter set to be 250 trees, with 21 variables considered at each split, ...
The value at risk of a portfolio with two securities can be determined by first calculating the portfolio's volatility. Multiply the square of the first asset's weight by the square of the first asset's standard deviation and add it to the square of the second asset's weight ...