pairwise

简明释义

英[ˈpeəˌwaɪz]美[peərˈwaɪz]

adj. 成对发生的

adv. 成对地；成双地

英英释义

单词用法

同义词

反义词

例句

1.There is inherent uncertainty and imprecision of the pairwise comparison process under fuzzy evaluation environment.

对于模糊环境下的评估，存在固有的不确定性和不精确性信息比较的情况。

2.But in many situations, pairwise testing is used to reduce the cost of test and is proven to be practical and effective for many software systems.

但是，在许多情况下两两测试可用于减少测试的成本，并对于许多软件系统在实践中证明是实用和有效的。

3.we can aggregate subtrees of the "plain" type pairwise. But I would prefer if Spark allowed a more EBNF-style grammar expression like this

我们可以成对地聚集“plain“类型的子树。

4.We study the properties of incompressible pairwise incompressible surfaces in knot exteriors.

讨论了纽结补中的不可压缩、分段不可压缩曲面的性质。

5.The existence of a best approximation to the pairwise comparison matrix from the set of consistent matrices is proved.

本文首先证明了成对比较矩阵在相容性矩阵集合中的最佳逼近的存在性和不唯一性。

6.These phage DNAs are in pairwise combinations and divided into two groups-with or without carrier DNA during the process of cotransfection.

比较其转化效率，结果基因组DNA转化率大于基因两个片段的共转化率，更大于不加携带者DNA的共转化率。

7.Database similarity search is based upon sequence alignment methods also used in pairwise sequence comparison.

数据库相似性搜索是基于序列比对方法，也被用在成对序列比较中。

8.The algorithm evaluates data points in a pairwise 成对的 manner to determine similarities.

该算法以成对的方式评估数据点以确定相似性。

9.In statistics, we often conduct pairwise 成对的 comparisons to analyze the differences between groups.

在统计学中，我们经常进行成对的比较来分析组之间的差异。

10.The study used a pairwise 成对的 testing approach to assess user preferences.

该研究使用了成对的测试方法来评估用户偏好。

11.To optimize the network, we examined pairwise 成对的 connections between nodes.

为了优化网络，我们检查了节点之间的成对的连接。

12.The researchers performed pairwise 成对的 analyses of variance to identify significant effects.

研究人员进行了成对的方差分析，以识别显著效应。

作文

In the realm of data analysis and statistics, one often encounters various methods and techniques to evaluate relationships between different variables. One such method is known as pairwise comparison. This technique involves comparing each pair of items or variables in a dataset to determine their differences or similarities. The concept of pairwise comparisons is widely used in many fields, including psychology, marketing research, and machine learning. By analyzing data in a pairwise manner, researchers can gain deeper insights into the relationships that exist among variables. For instance, consider a study aimed at understanding consumer preferences for different brands of soft drinks. Researchers might conduct a survey where participants are asked to compare two brands at a time. This pairwise approach allows them to capture nuanced preferences that might not be evident when asking participants to evaluate all brands simultaneously. Instead of overwhelming respondents with too many options at once, the pairwise method simplifies the decision-making process, making it easier to gather accurate data. Moreover, the pairwise technique can be particularly useful in statistical tests, such as ANOVA (Analysis of Variance). When researchers want to determine if there are significant differences between multiple groups, they can perform pairwise comparisons to identify which specific groups differ from each other. This is crucial for drawing meaningful conclusions from experimental data and ensuring that the results are reliable. Another area where pairwise analysis shines is in machine learning, particularly in classification tasks. In supervised learning, algorithms often need to evaluate the similarity or dissimilarity between data points. By employing pairwise distance metrics, such as Euclidean distance or cosine similarity, machine learning models can effectively classify data into distinct categories based on how closely related they are to one another. Furthermore, the pairwise approach can enhance the efficiency of algorithms. Instead of evaluating all possible combinations of data points, which can be computationally expensive, pairwise methods allow for targeted comparisons, streamlining the process and reducing the time required for analysis. In conclusion, the concept of pairwise comparisons plays a vital role in various fields of research and analysis. By breaking down complex datasets into manageable pairs, researchers and analysts can uncover valuable insights and make informed decisions. Whether in understanding consumer behavior, conducting statistical tests, or developing machine learning models, the pairwise technique proves to be an indispensable tool. As we continue to navigate an increasingly data-driven world, mastering the use of pairwise methods will undoubtedly enhance our ability to interpret information and draw meaningful conclusions from our findings.

在数据分析和统计的领域中，人们经常会遇到各种方法和技术来评估不同变量之间的关系。其中一种方法被称为成对比较。这种技术涉及在数据集中比较每一对项目或变量，以确定它们之间的差异或相似性。成对比较的概念在许多领域中得到了广泛应用，包括心理学、市场研究和机器学习。通过以成对的方式分析数据，研究人员可以更深入地了解变量之间存在的关系。 例如，考虑一个旨在理解消费者对不同品牌软饮料偏好的研究。研究人员可能会进行一项调查，让参与者逐对比较两个品牌。这种成对的方法使他们能够捕捉到微妙的偏好，这些偏好在要求参与者同时评估所有品牌时可能不会显现出来。与其让受访者一次性面对过多选项，不如成对的方法简化决策过程，使收集准确数据变得更加容易。 此外，成对技术在统计检验中尤为有用，例如方差分析（ANOVA）。当研究人员希望确定多个组之间是否存在显著差异时，他们可以进行成对比较，以识别哪些特定组彼此不同。这对于从实验数据中得出有意义的结论至关重要，并确保结果的可靠性。 另一个成对分析大放异彩的领域是机器学习，特别是在分类任务中。在监督学习中，算法通常需要评估数据点之间的相似性或不相似性。通过采用成对距离度量，例如欧几里得距离或余弦相似度，机器学习模型可以有效地根据数据点之间的相关性将数据分类为不同类别。 此外，成对方法可以提高算法的效率。与其评估所有可能的数据点组合（这可能计算开销很大），不如成对方法允许针对性比较，从而简化过程，减少所需的分析时间。 总之，成对比较的概念在各个研究和分析领域中扮演着重要角色。通过将复杂的数据集分解为可管理的对，研究人员和分析师可以发现有价值的见解并做出明智的决策。无论是在理解消费者行为、进行统计检验还是开发机器学习模型方面，成对技术都证明是一种不可或缺的工具。随着我们继续在日益数据驱动的世界中航行，掌握成对方法的使用无疑将增强我们解读信息和从发现中得出有意义结论的能力。