English(EN) The Difference Between Normalization and Regularization in Machine Learning

归一化与正则化：阐明关键的机器学习概念

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-07 17:31

归一化和正则化是机器学习中不同的概念，由于诸如“L2范数”之类的相似术语而常常被混淆。归一化是一个数据预处理步骤，它将输入特征缩放到可比较的范围，确保没有单个特征因其量级而占主导地位。这发生在模型训练之前。另一方面，正则化是一种在模型训练过程中用于惩罚模型复杂性并防止过拟合的技术。 AI

影响阐明了机器学习从业者的核心概念，提高了对数据准备和模型训练的理解。

排序理由文章解释了基础的机器学习概念，没有发布新的研究或产品。

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Towards AI TIER_1 English(EN) · Mohammad Soroush Esnaashari · 2026-09-07 17:31

机器学习中归一化与正则化的区别

<h3>Introduction: Untangling the Terms</h3><p>“Normalization” and “regularization” are two fundamental concepts in machine learning that sound related but play very different roles. Both can appear similar (especially when you hear “L2 norm” in both contexts), and this leads to c…