English(EN) Can Domain Generalization be Guaranteed in Small-Sample Learning?

新理论保证小样本学习中的领域泛化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-02 04:00

提出了一种用于小样本学习中领域泛化的新理论框架，解决了机器学习中因数据有限导致模型估计不稳定的根本性挑战。该研究首次为领域泛化场景下的结构化风险最小化（SRM）建立了理论保证，这些场景通常违反标准的独立同分布（i.i.d.）假设。研究结果推导了学习一致性和泛化误差界限，证明了在特定稳定性条件下的紧密性，并讨论了其在深度学习模型中的适用性。 AI

影响为鲁棒的领域泛化算法提供了理论基础，有望在数据有限的情况下提高AI模型的性能。

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arXiv cs.LG TIER_1 English(EN) · Hong Zheng · 2026-10-02 04:00

小样本学习中的域泛化能否得到保证？

arXiv:2609.39512v1 Announce Type: new Abstract: The small-sample learning problem remains a fundamental challenge in machine learning because limited training data lead to unstable model estimation and generalization. Structural Risk Minimization (SRM) has long been regarded as a…

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小样本学习中的域泛化能否得到保证？

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