Generalized cross-validation is a statistical technique used for estimating the predictive performance of a model by assessing its ability to generalize to unseen data. It extends traditional cross-validation methods by incorporating a penalty term that accounts for the complexity of the model, thus providing a more reliable estimate of its performance, particularly in the context of inverse problems and parameter estimation.
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