Structural risk minimization


Structural risk minimization is an inductive principle of use in machine learning. Commonly in machine learning, a generalized model must be selected from a finite data set, with the consequent problem of overfitting - the model becoming too strongly tailored to the particularities of the training set and generalizing poorly to new data. The SRM principle addresses this problem by balancing the model's complexity against its success at fitting the training data.
In practical terms, Structural Risk Minimization is implemented by minimizing, where is the train error, the function is called a regularization function, and is a constant. is chosen such that it takes large values on parameters that belong to high-capacity subsets of the parameter space. Minimizing in effect limits the capacity of the accessible subsets of the parameter space, thereby controlling the trade-off between minimizing the training error and minimizing the expected gap between the training error and test error.
The SRM principle was first set out in a 1974 paper by Vladimir Vapnik and Alexey Chervonenkis and uses the VC dimension.