subsampling

简明释义

英[ˈsʌbˌsæmplɪŋ]美[ˈsʌbˌsæmplɪŋ]

n. 二次抽样

v. 对……做两次抽样（subsample 的 ing 形式）

英英释义

单词用法

同义词

反义词

例句

1.Based on the remainder theorem and direct dechirp operation, an algorithm for estimation of chirp rate and initial frequency of the subsampling wideband LFM signal unambiguously is presented.

基于中国余数定理解频率模糊和解线调方法，提出一种无模糊欠采样的宽带线性调频信号的调频斜率和初始频率的估计算法。

2.A novel subsampling based robust image watermarking scheme was proposed.

提出了一种新的基于子采样的图像数字水印算法。

3.Based on the remainder theorem and direct dechirp operation, an algorithm for estimation of chirp rate and initial frequency of the subsampling wideband LFM signal unambiguously is presented.

基于中国余数定理解频率模糊和解线调方法，提出一种无模糊欠采样的宽带线性调频信号的调频斜率和初始频率的估计算法。

4.Then the author mentioned that the incomplete subsampling is one of reliability preprocessing methods, it includes instantaneous failure, data induction and Johnson method, etc.

不完全子样法是可靠性预处理的一种方法，包括瞬间故障率法、数据归定法、约翰逊法等。

5.A receiver architecture is presented that utilizes the subsampling concept to down-convert the IF signal to a lower IF before been digitized.

提出了一个欠采样中频收发器的体系结构，它在中频数字化以前先通过欠采样将中频频率变到一个较低的频率。

6.The traditional subsampling based information hiding algorithm can not resist the permutation attack. In this paper, an improved algorithm is proposed to resist the permutation attack.

以往的基于子采样的信息隐藏算法无法抵抗置乱攻击，本文提出了一个改进算法，实验表明该算法能有效抵抗置乱攻击。

7.The algorithm performs better when trained on subsampling rather than the full dataset.

该算法在训练时使用子采样而不是完整数据集时表现更好。

8.By subsampling the data, we were able to speed up the processing time significantly.

通过子采样数据，我们能够显著加快处理时间。

9.In large datasets, subsampling can help reduce computational costs.

在大型数据集中，子采样可以帮助降低计算成本。

10.The research team used subsampling to select a manageable number of samples for analysis.

研究小组使用子采样来选择一个可管理的样本数量进行分析。

11.To avoid bias, it is crucial to perform random subsampling.

为了避免偏差，进行随机子采样至关重要。

作文

In the field of data analysis, one of the crucial techniques employed is subsampling. This method involves taking a smaller sample from a larger dataset to make the analysis more manageable and efficient. The idea behind subsampling (子抽样) is to reduce the amount of data while still retaining the essential characteristics of the original dataset. This approach can be particularly useful in scenarios where dealing with the full dataset is computationally expensive or time-consuming. For instance, consider a situation where a researcher is analyzing social media data that spans several years and includes millions of posts. Analyzing the entire dataset might require significant computational resources and time. By using subsampling (子抽样), the researcher can select a representative subset of the data, which can provide insights without the need for extensive processing. This not only saves time but also allows for quicker iterations in research and analysis. Moreover, subsampling (子抽样) can help in avoiding overfitting in machine learning models. When a model is trained on a very large dataset, it may learn noise and outliers that do not generalize well to new data. By applying subsampling (子抽样), researchers can create a more balanced training set, which leads to better performance on unseen data. This technique is especially valuable in cases where the dataset is imbalanced, meaning that certain classes of data are underrepresented. Another important aspect of subsampling (子抽样) is its application in survey sampling. In surveys, it is often impractical to collect data from every individual in a population. Instead, researchers use subsampling (子抽样) to select a smaller group that is representative of the larger population. This helps in making generalizations about the entire population based on the analysis of the sample. For example, political polls often rely on subsampling (子抽样) to gauge public opinion without needing to survey every voter. However, it is important to note that subsampling (子抽样) must be done carefully to ensure that the sample accurately represents the larger dataset. If the sampling method is biased, it can lead to incorrect conclusions. Techniques such as stratified sampling, where the population is divided into subgroups before sampling, can help mitigate this risk. In conclusion, subsampling (子抽样) is a powerful tool in data analysis, enabling researchers to draw meaningful insights from large datasets while optimizing resources. Whether in the context of big data analytics, machine learning, or survey research, understanding and applying subsampling (子抽样) effectively is essential for achieving accurate and reliable results. As the volume of data continues to grow, mastering techniques like subsampling (子抽样) will become increasingly important for data scientists and analysts alike.

在数据分析领域，采用的关键技术之一是子抽样。这种方法涉及从较大的数据集中提取一个较小的样本，以使分析更易于管理和高效。子抽样的背后理念是减少数据量，同时仍然保留原始数据集的基本特征。这种方法在处理完整数据集计算成本高或耗时的情况下尤其有用。例如，考虑一个研究人员分析社交媒体数据的情况，这些数据跨越数年并包含数百万条帖子。分析整个数据集可能需要大量计算资源和时间。通过使用子抽样，研究人员可以选择一个具有代表性的子集，从而在不需要大量处理的情况下提供见解。这不仅节省了时间，还允许研究和分析中的快速迭代。此外，子抽样可以帮助避免机器学习模型的过拟合。当模型在非常大的数据集上进行训练时，它可能会学习到噪声和离群值，这些都无法很好地泛化到新数据中。通过应用子抽样，研究人员可以创建一个更平衡的训练集，从而在未见过的数据上获得更好的表现。这种技术在数据集不平衡的情况下尤其有价值，即某些类别的数据代表性不足。子抽样的另一个重要方面是它在调查抽样中的应用。在调查中，从每个个体收集数据往往是不切实际的。因此，研究人员使用子抽样来选择一个较小的、代表更大群体的组。这有助于基于样本的分析对整个群体做出概括。例如，政治民调通常依赖于子抽样来衡量公众意见，而无需调查每位选民。然而，需要注意的是，子抽样必须谨慎进行，以确保样本准确代表较大的数据集。如果抽样方法存在偏差，可能导致错误结论。分层抽样等技术可以帮助降低这种风险，其中将总体分成子组，然后进行抽样。总之，子抽样是数据分析中的一种强大工具，使研究人员能够从大型数据集中提取有意义的见解，同时优化资源。无论是在大数据分析、机器学习还是调查研究的背景下，理解和有效应用子抽样对于实现准确可靠的结果至关重要。随着数据量的不断增长，掌握像子抽样这样的技术将变得越来越重要，数据科学家和分析师都应如此。