Let $\mathcal{H}$ be the hypothesis space defined by Few-Shot Learning, $\hat{h}$ be the model that minimizes expected risk (which may not be in $\mathcal{H}$), $h^{*}$ be the model in $\mathcal{H}$ that minimizes expected risk, and $h_I$ be the model in $\mathcal{H}$ that minimizes empirical risk. The total error can be decomposed as: $\mathcal{E}[R(h_I) - R(\hat{h})] = \mathcal{E}[R(h^*) - R(\hat{h})] + \mathcal{E}[R(h_I) - R(h^*)]$ The first term is the Approximation Error, which is determined by the hypothesis space $\mathcal{H}$. The second term is the Estimation Error, which is affected by the quantity and quality of the training data. The core issue of few-shot learning is that the small training sample size leads to a high estimation error, resulting in an unreliable empirical minimizer $h_I$.