What is a structural risk minimization?
An algorithm that balances two goals: - The need to build the most predictive model (for example, lowest loss).
structural risk minimization explained in plain English
An algorithm that balances two goals: - The need to build the most predictive model (for example, lowest loss). - The need 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. Contrast with empirical risk minimization.
Example
Practitioners refer to structural risk minimization when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.
People also read
- A/B testing
A statistical way of comparing two (or more) techniques—the A and the B.
- ablation
A technique for evaluating the importance of a feature or component by temporarily removing it from a model.
- accuracy
The number of correct classification predictions divided by the total number of predictions.
- activation function
A function that enables neural networks to learn nonlinear (complex) relationships between features and the label.
- active learning
A training approach in which the algorithm chooses some of the data it learns from.
- adaptation
Synonym for tuning or fine-tuning.
- agglomerative clustering
See hierarchical clustering.
- anomaly detection
The process of identifying outliers.
- area under the PR curve
See PR AUC (Area under the PR Curve).
- area under the ROC curve
See AUC (Area under the ROC curve).