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prai-related-work

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在为《模式识别与人工智能》(Pattern Recognition and Artificial Intelligence, PR&AI) 撰写相关工作/文献综述并确立创新性时调用。本刊要求成果具原创创新价值，相关工作须 delta 优先——把本文相对最接近工作的增量讲清，而非文献罗列。覆盖如何组织相关工作、如何在模式识别/机器学习/视觉/NLP 语境中定位 delta、如何用 GB/T 7714 规范引用中英文文献并给中文文献英文对照、如何避免"综述式罗列"与"漏引近作"两类退稿风险。用于把创新性写到经得起外审质疑的水准。

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

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# 《模式识别与人工智能》相关工作与创新性定位

本技能帮助在《模式识别与人工智能》(Pattern Recognition and Artificial Intelligence, PR&AI) 的稿件
中把相关工作写成**创新性论证**而非文献堆砌。本刊只发表具创新价值的成果，外审会重点质疑"与已有
工作的区别"。事实核验日期 2026-07-09，见 `resources/official-source-map.md`。

## 一、delta 优先的组织原则

相关工作的目的不是证明"我读得多"，而是**定位本文贡献相对已有工作的增量(delta)**。每讨论一类已有
方法，都要回答：它度量/解决了什么？还缺什么？本文如何补上？

- 按**问题维度**而非时间顺序组织（如"按噪声鲁棒性分类的方法""按是否需要标注分类的方法"）。
- 每一类结尾点出"该类的共同局限"，为本文 delta 铺垫。
- 避免"A 做了…B 做了…C 做了…"的流水账。
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