文章目录
正则化（Regularization）和正则表达式（Regular Expression）不是同一个概念，它们是两个完全不同的术语，应用于不同的领域。
1. 正则化（Regularization）
- 领域：机器学习/统计学。
- 定义：正则化是一种用于防止模型过拟合（Overfitting）的技术。它通过在损失函数中添加一个惩罚项（如 L1 正则化、L2 正则化），限制模型参数的大小，从而降低模型复杂度。
- 例子：训练神经网络时，使用 L2 正则化（权重衰减）来约束权重值。
2. 正则表达式（Regular Expression）
- 领域：计算机科学/文本处理。
- 定义：正则表达式是一种用于描述字符串匹配模式的语法规则，常用于字符串搜索、替换或验证。
- 例子 ：用
\d{3}-\d{4}匹配电话号码格式（如123-4567）。
关键区别
- 目的：正则化是优化模型泛化能力；正则表达式是处理文本模式匹配。
- 应用场景：前者用于机器学习模型训练，后者用于字符串操作。
为什么名字相近？
两者英文名均源自数学中的"正则"（Regular），但中文翻译保留了相同前缀，导致字面混淆。实际应用中需注意区分。
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