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An algorithm that balances two goals:
The desire to build the most predictive model (for example, lowest loss).
The desire to keep the model as simple as possible (for example, strong regularization).
For example, a function that minimizes loss+regularization on the training set is a structural risk minimization algorithm.
For more information, see http://www.svms.org/srm/.
Contrast with empirical risk minimization.
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