Regularize
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REGULARIZE中文(简体)翻译：剑桥词典 - Cambridge Dictionary
网页verb [ T ] (UK usually regularise) uk / ˈreɡ.jə.l ə r.aɪz / us / ˈreɡ.jə.lɚ.aɪz / Add to word list. to change a situation or system so that it obeys laws or is based on reason. 使合法化；使正 …
机器学习必知必会：正则化 - 知乎 - 知乎专栏
网页机器学习中经常会在损失函数中加入正则项，称之为正则化（Regularize）。 目的：防止模型过拟合. 原理：在损失函数上加上某些规则（限制），缩小解空间，从而减少求出过拟合解的可能性. 例子：以最简单的线性模型为例. y=X\beta + \epsilon
笔记 | 什么是Regularization - 知乎 - 知乎专栏
网页Regularization：在现有Features不变情况下，降低部分不重要Features的影响力。. 这个方法有助于有很多Features且每个Features都有贡献的Neural Network避免Overfitting。. Regularization不是新鲜的词，我这里主要记录其在神经网络中的应用。. 复杂模型的overfitting问题. 第一次听 ...
总结-深度学习中的正则化方法（regularization） - 知乎专栏
网页总结-深度学习中的正则化方法（regularization）. 深度学习面临的非常严重的一个问题就是过拟合（overfitting）。. 通过一些正则化的方法，可以消除过拟合，从而使我们的模型能够得到更好的效果。. 1. 什么是正则化. 这张图，我想接触过机器学习的朋友们应该都看 ...
Regularize Definition & Meaning - Merriam-Webster
网页The meaning of REGULARIZE is to make regular by conformance to law, rules, or custom. How to use regularize in a sentence.
Overfitting: L2 regularization | Machine Learning - Google …
网页Last updated 2024-08-13 UTC. Learn how the L2 regularization metric is calculated and how to set a regularization rate to minimize the combination of loss and complexity during model training, or...
Regularization. What, Why, When, and How?
网页2020年10月23日 · Regularization is a method to constraint the model to fit our data accurately and not overfit. It can also be thought of as penalizing unnecessary complexity in our model. There are mainly 3 types of….
Everything You Need To Know About Regularization
网页2023年1月25日 · Regularization is a technique used to prevent overfitting and improve the performance of models. In this post, we’ll break down the different types of regularization and how you can use them to improve your models. Besides, you learn when to apply the different types. Regularization in machine learning means ‘simplifying the outcome’.
Regularization - SpringerLink
网页Definition. Regularization plays a key role in many machine learning algorithms. Exactly fitting a model to the training data is generally undesirable, because it will fit the noise in the training examples ( overfitting), and is doomed to predict (generalize) poorly on unseen data.
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