normalizer

简明释义

英[ˈnɔːməlaɪzə(r)]美[ˈnɔrmlˌaɪzər]

n. 标准化者；正规化者

英英释义

单词用法

同义词

反义词

例句

1.Based on correlation coefficients and principal component analysis, fe and al were selected as the concentration normalizer for the different trace metals.

由相关系数和主成份分析，选择铁和铝作为沉积物中痕量金属浓度的均一化元素。

2.When there is a need for rarer EI Patterns that is not supported by the EIP component (fully dynamic routing, Return Address, Content Enricher, Normalizer…)

当需要一种罕见的EI模式且这个模式不被EIP组件支持的时候（全动态路由、返回地址、内容丰富器、规范化器……）

3.Normalizer: This class provides functions to normalize the output of transliteration function into a composed or decomposed form.

Normalizer：这个类提供一些函数，将音译函数的输出规范化为一种合成或分解格式。

4.The main classes that support transliteration capabilities are Transliterator and Normalizer.

支持音译功能的主要类是 Transliterator和Normalizer。

5.Burnside asserts, if any sylow p-subgroup P of a finite G lies in the center of its normalizer, then G isp-nilpotent.

群论研究的一个重要问题是对有限群的p—幂零性对有限群结构的影响。

6.When there is a need for rarer EI Patterns that is not supported by the EIP component (fully dynamic routing, Return Address, Content Enricher, Normalizer…)

当需要一种罕见的EI模式且这个模式不被EIP组件支持的时候（全动态路由、返回地址、内容丰富器、规范化器……）

7.To solve this, the intl library provides the Normalizer class.

为了解决这个问题，intl库提供了normalizer类。

8.A normalizer 归一化器 is essential for algorithms that rely on distance measurements.

对于依赖距离测量的算法，normalizer 归一化器 是必不可少的。

9.Using a normalizer 归一化器 can reduce the impact of outliers in your dataset.

使用normalizer 归一化器 可以减少数据集中异常值的影响。

10.Before training the neural network, we applied a normalizer 归一化器 to the input features.

在训练神经网络之前，我们对输入特征应用了normalizer 归一化器。

11.The normalizer 归一化器 helped improve the model's accuracy by standardizing the input data.

这个normalizer 归一化器 通过标准化输入数据来提高模型的准确性。

12.In data preprocessing, a normalizer 归一化器 is used to scale the features to a common range.

在数据预处理过程中，normalizer 归一化器 用于将特征缩放到一个共同的范围。

作文

In the realm of data processing and analysis, the term normalizer (标准化器) plays a crucial role in ensuring that datasets are consistent and comparable. A normalizer is a tool or algorithm used to adjust values in a dataset to a common scale, without distorting differences in the ranges of values. This process is essential in various fields such as statistics, machine learning, and data mining. Without proper normalization, data can lead to misleading results and interpretations. For instance, consider a scenario where we are analyzing the performance of students across different subjects. If we have scores ranging from 0 to 100 in Mathematics and scores from 0 to 10 in Art, it would be challenging to compare these scores directly. Here, a normalizer (标准化器) would help convert these scores into a common format, allowing us to make fair comparisons. By applying normalization techniques, we can transform the data into a uniform scale, facilitating better analysis and decision-making.Normalization can take various forms, including min-max scaling, z-score standardization, and decimal scaling. Each of these methods has its own advantages and is suitable for different types of data. For example, min-max scaling transforms the data into a range between 0 and 1, which is particularly useful when the dataset has varying units or scales. On the other hand, z-score standardization centers the data around the mean with a standard deviation of one, making it ideal for datasets that follow a normal distribution.The importance of a normalizer (标准化器) extends beyond mere data preparation. In machine learning, for example, many algorithms rely on normalized data to function correctly. Algorithms like k-nearest neighbors (KNN) and support vector machines (SVM) are sensitive to the scale of the input features. If the features are not normalized, the model may perform poorly, as it could give undue weight to features with larger ranges. Therefore, implementing a normalizer is not just a best practice; it is often a prerequisite for successful model training and evaluation.Moreover, normalization contributes to the interpretability of the results. When data is presented in a normalized format, stakeholders can easily understand the relative performance or significance of different variables. This clarity is especially vital in fields like healthcare, finance, and social sciences, where decisions based on data can have profound implications.In conclusion, the concept of a normalizer (标准化器) is fundamental in the data analysis process. It ensures that data is consistent, comparable, and interpretable. As we continue to generate and analyze vast amounts of data, the role of normalizers will only become more critical. Embracing normalization techniques not only enhances the quality of our analyses but also empowers us to make informed decisions based on reliable data. Thus, understanding and utilizing a normalizer is essential for anyone involved in data-centric fields.

在数据处理和分析领域，术语normalizer（标准化器）在确保数据集一致性和可比较性方面发挥着至关重要的作用。normalizer是用于调整数据集中值以达到共同规模的工具或算法，而不会扭曲值范围之间的差异。这一过程在统计学、机器学习和数据挖掘等多个领域中都是必不可少的。如果没有适当的标准化，数据可能会导致误导性的结果和解释。例如，考虑一个我们正在分析学生在不同科目表现的场景。如果我们有数学的分数范围从0到100，而艺术的分数范围从0到10，那么直接比较这些分数将会很困难。在这里，normalizer（标准化器）将帮助将这些分数转换为共同格式，从而使我们能够进行公平的比较。通过应用标准化技术，我们可以将数据转换为统一的尺度，从而促进更好的分析和决策。标准化可以采取多种形式，包括最小-最大缩放、z-score标准化和小数缩放。这些方法各有优点，适用于不同类型的数据。例如，最小-最大缩放将数据转换为0到1之间的范围，这在数据集具有不同单位或尺度时特别有用。另一方面，z-score标准化使数据围绕均值中心并具有标准差为1，这对于遵循正态分布的数据集尤其理想。normalizer（标准化器）的重要性不仅限于数据准备。在机器学习中，例如，许多算法依赖于标准化数据才能正常工作。像k最近邻（KNN）和支持向量机（SVM）这样的算法对输入特征的尺度非常敏感。如果特征没有标准化，模型可能会表现不佳，因为它可能会对范围较大的特征给予过多的权重。因此，实施normalizer不仅是一种最佳实践，它通常是成功模型训练和评估的先决条件。此外，标准化有助于结果的可解释性。当数据以标准化格式呈现时，利益相关者可以轻松理解不同变量的相对表现或重要性。这种清晰度在医疗保健、金融和社会科学等领域尤其重要，因为基于数据做出的决策可能会产生深远的影响。总之，normalizer（标准化器）的概念在数据分析过程中是基础。它确保数据的一致性、可比较性和可解释性。随着我们继续生成和分析大量数据，normalizer的角色只会变得更加重要。采用标准化技术不仅提高了我们分析的质量，还使我们能够基于可靠的数据做出明智的决策。因此，理解和利用normalizer对于任何参与数据中心领域的人来说都是至关重要的。