Читать книгу Machine Learning for Tomographic Imaging - Professor Ge Wang - Страница 52

The mean squared error (MSE) is widely used in linear regression as the performance measure. The method for minimizing MSE is called ordinary least squares (OSL), which minimizes the sum of squared distances from data points to the regression line. The MSE function takes all errors into consideration with the same or different weights. The standard form of the MSE function is defined as

L=1n∑i=1ny(i)−yˆ(i)2,(3.18)

where (y(i)−yˆ(i)) is also referred to residual and is used to minimize the sum of squared residuals. Note that more rigorously, the normalizing factor 1/n should be 1/(n − 1) to eliminate any bias of the estimator L.