Examples include weight penalties, dropout, data augmentation, and early stopping. Each method limits memorization or injects variation so the learned solution depends on more general patterns.
Regularization is any training constraint or penalty intended to improve performance on unseen data rather than only the training set.
Examples include weight penalties, dropout, data augmentation, and early stopping. Each method limits memorization or injects variation so the learned solution depends on more general patterns.