Empirical Risk Minimization
In general, the risk cannot be computed because the distribution is unknown to the learning algorithm (this situation is referred to as agnostic learning). However, we can compute an approximation, called empirical risk, by averaging the loss function on the training set:
Empirical risk minimization principle states that the learning algorithm should choose a hypothesis which minimizes the empirical risk:
Thus the learning algorithm defined by the ERM principle consists in solving the above optimization problem.
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