Inverse Problems
Generalized cross-validation is a method used to estimate the performance of a model by assessing how well it generalizes to unseen data. It extends traditional cross-validation techniques by considering the effect of regularization and allows for an efficient and automated way to select the optimal regularization parameter without needing a separate validation set. This method is particularly useful in scenarios where overfitting can occur, such as in regularization techniques.
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