Problems tagged with "empirical risk minimization"

Let \(\mathcal D\) be a data set of points in \(\mathbb R^d\). Suppose a linear prediction rule \(H_1(\vec x)\) is fit to the data by minimizing the risk with respect to the square loss, and suppose another linear prediction rule \(H_2(\vec x)\) is fit to the data by minimizing the risk with respect to the absolute loss.

True or False: \(H_1\) and \(H_2\) are guaranteed to be the same; that is, \(H_1(\vec x) = H_2(\vec x)\) for all inputs \(\vec x\).