normalization

简明释义

英[ˌnɔːməlaɪˈzeɪʃn]美[ˌnɔːrmələˈzeɪʃn]

n. 正常化，标准化

英英释义

单词用法

同义词

反义词

例句

1.Suffice it to say that anyone wanting to create a database system must be familiar with the concept of normalization.

任何想要创建数据库系统的人们必需熟悉标准化的概念,只要说明这个就够了。

2.RDA helps architects in normalization of the developed model.

RDA帮助架构师对开发的模型进行标准化。

3.The normalization checking attribute, if set to on, will normalize the input text if necessary.

如果打开了规范化检查属性，那么会在必要时对输入文本进行规范化。

4.Filtering rules are not the same thing as the normalization rules that aim at reducing data redundancy.

筛选规则与针对减少数据冗余的标准化规则不是一回事。

5.China's economic growth has remained resilient as the macro stance moved towards normalization.

在宏观政策立场趋于正常的情况下中国经济增长仍稳健。

6.For example, the example pictured in Figure 2 probably has a normalization problem.

例如，图2所描绘的示例可能有标准化问题。

7.This process, called normalization, restructures the data to limit redundancy.

这个过程称为规范化(normalization)，重组数据以限制冗余。

8.In statistics, normalization is used to adjust values measured on different scales.

在统计学中，标准化用于调整在不同尺度上测量的值。

9.The normalization of the data improved the accuracy of our predictions.

数据的标准化提高了我们预测的准确性。

10.The process of normalization helps to reduce redundancy in the database.

这个标准化过程有助于减少数据库中的冗余。

11.After normalization, the scores were much easier to compare.

经过标准化后，分数更容易进行比较。

12.The normalization process can greatly enhance the performance of machine learning algorithms.

这个标准化过程可以大大提高机器学习算法的性能。

作文

Normalization is a concept that plays a crucial role in various fields, including statistics, data analysis, and even social sciences. In essence, normalization refers to the process of adjusting values measured on different scales to a common scale. This is particularly important when comparing datasets that may have been collected under different conditions or using different methods. For example, in statistical research, raw data can often be skewed by outliers or varying measurement units, which can lead to misleading conclusions. By applying normalization, researchers can ensure that their data is more comparable and meaningful.In the realm of data analysis, normalization is essential for preparing data for machine learning algorithms. Many algorithms assume that the input data is centered around zero and has unit variance. If the data is not normalized, it can lead to poor performance of the model. For instance, consider a dataset that includes both height (in centimeters) and weight (in kilograms). If we were to feed this data into a machine learning model without applying normalization, the model might give undue importance to the weight variable simply because it has larger numerical values compared to height. Therefore, applying normalization techniques such as min-max scaling or z-score standardization can help mitigate this issue, allowing the model to learn patterns more effectively.Beyond technical applications, normalization also has social implications. In social sciences, normalization can refer to the process through which certain behaviors or beliefs become accepted as the norm within a society. This can be seen in various contexts, from the way that cultural practices are adopted to how societal standards evolve over time. For instance, the increasing acceptance of diverse family structures can be viewed as a form of normalization of what was once considered non-traditional. Understanding this aspect of normalization helps us recognize how societal norms are not fixed but rather dynamic and subject to change.In conclusion, normalization is a multifaceted concept that transcends its mathematical origins. Whether in data analysis, statistics, or social sciences, the idea of bringing different elements to a common standard is vital for ensuring clarity, fairness, and accuracy. As we continue to navigate an increasingly complex world filled with diverse data and perspectives, the importance of normalization cannot be overstated. It allows us to make informed decisions based on reliable comparisons and fosters a deeper understanding of the norms that shape our societies. By embracing normalization, we can enhance our analytical capabilities and promote inclusivity in our social frameworks.

标准化是一个在多个领域中发挥关键作用的概念，包括统计学、数据分析甚至社会科学。其本质上，标准化指的是将不同尺度上测量的值调整到一个共同尺度的过程。这在比较可能在不同条件下收集或使用不同方法的数据集时尤为重要。例如，在统计研究中，原始数据往往会受到异常值或不同测量单位的影响，这可能导致误导性的结论。通过应用标准化，研究人员可以确保他们的数据更具可比性和意义。在数据分析领域，标准化对于准备数据以供机器学习算法使用至关重要。许多算法假设输入数据围绕零中心并具有单位方差。如果数据没有经过标准化，可能会导致模型性能不佳。例如，考虑一个包含身高（以厘米为单位）和体重（以千克为单位）的数据集。如果我们在未应用标准化的情况下将此数据输入机器学习模型，模型可能会因为体重变量的数值较大而给予其过多的重视。因此，应用如最小-最大缩放或z-score标准化等标准化技术可以帮助缓解这一问题，使模型能够更有效地学习模式。超越技术应用，标准化也具有社会意义。在社会科学中，标准化可以指某些行为或信仰在社会中被接受为规范的过程。这可以在各种背景中看到，从文化实践的采纳到社会标准随时间演变的方式。例如，对多样家庭结构的日益接受可以被视为对曾被认为非传统的标准化。理解标准化这一方面有助于我们认识到社会规范并不是固定的，而是动态的，受变化的影响。总之，标准化是一个多面的概念，超越了其数学起源。无论是在数据分析、统计学还是社会科学中，将不同元素带到共同标准的理念对于确保清晰、公平和准确至关重要。随着我们继续在一个充满多样数据和观点的日益复杂的世界中航行，标准化的重要性不容低估。它使我们能够基于可靠的比较做出明智的决策，并促进我们社会框架中的包容性。通过拥抱标准化，我们可以增强我们的分析能力，并推动社会的包容性。