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prai-experiments

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在为《模式识别与人工智能》(Pattern Recognition and Artificial Intelligence, PR&AI) 设计与呈现模式识别/机器学习实验证据时调用。覆盖研究问题(RQ)拆解、公平基线与相同划分/算力、评测指标选择(准确率/F1/mAP/IoU/AUC 等)、多次运行的均值±标准差与统计显著性检验、消融实验、复杂度与开销报告、数据泄漏与评测污染的防范，以及把每条主张绑定到证据的呈现方式。用于把 AI 细分方向的实证部分做到经得起外审对"基线是否公平、结果是否可信、消融是否充分"的质询。

Pattern-Recognition-and-Artificial-Intelligence-Skills/skills/prai-experiments/SKILL.md

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# 《模式识别与人工智能》实验设计与呈现

本技能把《模式识别与人工智能》(Pattern Recognition and Artificial Intelligence, PR&AI) 论文的实证
部分落到可执行清单。本刊聚焦模式识别与机器学习，外审对实验严谨性要求高：公平基线、统计口径、消融、
开销缺一易被质疑。事实核验日期 2026-07-09，见 `resources/official-source-map.md`。

## 一、从主张到 RQ

先把贡献拆成可检验的研究问题(RQ)，每个 RQ 对应一组实验：

- RQ1（有效性）：本文方法是否在目标任务上优于最强基线？
- RQ2（机制）：哪个组件带来提升？（→消融）
- RQ3（鲁棒性）：在噪声/长尾/小样本/跨域等更难设定下是否仍有效？
- RQ4（开销）：精度提升的时间/显存/参数代价如何？
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