resample

简明释义

英[ˌriːˈsæmpl]美[ˌriːˈsæmpl]

n. 重新取样

英英释义

单词用法

同义词

反义词

例句

1.Third, Shearing the data and bilinear interposed value resample, then image composition to produce the middle image. And last, projecting the middle image, and warping to produce the final image.

经过错切和二线性插值重采样，再进行图像合成生成中间图像：对中间图像进行投影变形生成结果图像。

2.Third, Shearing the data and bilinear interposed value resample, then image composition to produce the middle image. And last, projecting the middle image, and warping to produce the final image.

经过错切和二线性插值重采样，再进行图像合成生成中间图像：对中间图像进行投影变形生成结果图像。

3.With the complete contour information, we present a convolution approach, which utilizes an appropriate linear interpolation to resample the contour to calculate pointwise curvature.

在获得轮廓的完整信息后，文中提出采用卷积积分的方法，通过线性插值对轮廓进行重采样来计算各离散点曲率。

4.This method adopted DDPF to estimate the path of robot and DDF to estimate the landmark position. It also adopted selectable resample to lower the probability of sample deletion.

该方法采用DDPF估计机器人路径，采用DDF估计陆标位置，同时采用可选重采样以降低样本贫化的概率。

5.Experiments show that, the algorithms not only have a certain amount of anti-attack capability, but also have some anti-noise and anti-resample performance.

实验表明，算法具有了一定的抗攻击能力，同时还具有一定的抗噪和抗重采样性能。

6.The platform includes MIT-BIH database and its resample database, a 12 channel synchronous ECG database and a virtual ECG generator.

研究平台包括了MIT-BIH数据库及其重采样数据库、同步12导数据库、虚拟心电信号发生器。

7.They test, resample, and retest equipment or systems until an "acceptable" residue level is attained.

他们要检测，重新取样，重新检测设备或系统，直到达到被认可的残留物级别。

8.To improve the accuracy of our analysis, we decided to resample the data set to include more recent information.

为了提高我们分析的准确性，我们决定重新抽样数据集，以包含更近期的信息。

9.The audio engineer had to resample the track to match the new tempo.

音频工程师必须重新采样音轨以匹配新的节奏。

10.In machine learning, it's common to resample training data to balance classes.

在机器学习中，通常会重新抽样训练数据以平衡类别。

11.We will resample the images to ensure they fit the required resolution.

我们将重新采样图像，以确保它们符合所需的分辨率。

12.The research team chose to resample the survey responses for a more representative sample.

研究团队选择重新抽样调查响应，以获得更具代表性的样本。

作文

In the field of data analysis and statistics, the term resample refers to the process of taking repeated samples from a dataset to assess the variability of a statistic or to improve the accuracy of estimates. This technique is especially useful when dealing with small sample sizes or when the underlying distribution of the data is unknown. By applying resample methods, analysts can generate additional data points that help in understanding the behavior of the original dataset. One common method of resample is known as bootstrapping. In this approach, we take a dataset and repeatedly draw samples from it, with replacement. This means that each time we select a data point, we put it back into the pool for potential selection again. This allows for the creation of numerous simulated samples, which can then be used to calculate various statistics, such as the mean, median, or standard deviation. The results from these simulated samples can provide a more robust estimate of the population parameters, leading to better decision-making. Another method related to resample is cross-validation, which is commonly used in machine learning. In this context, the dataset is divided into several subsets, and the model is trained on some of these while being tested on others. This process is repeated multiple times, and the performance metrics are averaged over all iterations. Cross-validation helps in evaluating how well a model generalizes to unseen data, thus preventing overfitting. By utilizing resample techniques like cross-validation, data scientists can ensure that their models are reliable and effective. The importance of resample cannot be overstated, particularly in research where conclusions must be drawn from limited data. Researchers often face challenges when their sample size is not large enough to provide statistically significant results. By applying resample strategies, they can create a more comprehensive view of their data, allowing them to make more informed conclusions. This is particularly crucial in fields such as medicine, where clinical trials may involve only a small number of participants. Additionally, resample techniques can also aid in hypothesis testing. When researchers want to determine if a certain effect exists, they can use resample methods to create a distribution of the test statistic under the null hypothesis. This allows them to compare their observed statistic against this distribution to assess the significance of their results. By doing so, they can better understand the likelihood of observing their data if the null hypothesis were true. In conclusion, the concept of resample plays a vital role in the realm of statistics and data analysis. It provides valuable tools for researchers and analysts to enhance their understanding of data, improve the accuracy of their estimates, and ultimately make more informed decisions. Whether through bootstrapping, cross-validation, or hypothesis testing, resample techniques empower professionals to navigate the complexities of data with greater confidence. As we continue to generate and collect vast amounts of data, mastering the art of resample will be essential for anyone looking to derive meaningful insights from their analyses.

在数据分析和统计学领域，术语resample指的是从数据集中多次抽取样本的过程，以评估统计量的变异性或提高估计的准确性。这种技术在处理小样本大小或当数据的基础分布未知时尤其有用。通过应用resample方法，分析人员可以生成额外的数据点，帮助理解原始数据集的行为。 一种常见的resample方法称为自助法。在这种方法中，我们从数据集中反复抽取样本，并允许重复选择。这意味着每次选择一个数据点后，我们将其放回池中，以便再次选择。这允许创建大量模拟样本，这些样本可以用于计算各种统计量，例如均值、中位数或标准差。这些模拟样本的结果可以提供对总体参数的更稳健估计，从而促进更好的决策。 与resample相关的另一种方法是交叉验证，这在机器学习中很常见。在这种情况下，数据集被分成几个子集，模型在其中一些子集上进行训练，而在其他子集上进行测试。这个过程会重复多次，性能指标在所有迭代中取平均。交叉验证有助于评估模型如何推广到未见过的数据，从而防止过拟合。通过利用像交叉验证这样的resample技术，数据科学家可以确保他们的模型可靠且有效。 resample的重要性不容小觑，特别是在研究中必须从有限数据中得出结论时。研究人员在样本量不足以提供统计显著结果时常常面临挑战。通过应用resample策略，他们可以创建对数据的更全面视图，使他们能够做出更明智的结论。这在医学等领域尤为重要，因为临床试验可能涉及的参与者数量很少。 此外，resample技术还可以帮助假设检验。当研究人员想要确定某种效应是否存在时，他们可以使用resample方法创建在零假设下检验统计量的分布。这使他们能够将观察到的统计量与该分布进行比较，以评估结果的显著性。通过这样做，他们可以更好地理解在零假设为真时观察到数据的可能性。 总之，resample的概念在统计学和数据分析领域发挥着至关重要的作用。它为研究人员和分析师提供了宝贵的工具，以增强对数据的理解，提高估计的准确性，并最终做出更明智的决策。无论是通过自助法、交叉验证还是假设检验，resample技术使专业人员能够更自信地驾驭数据的复杂性。随着我们继续生成和收集大量数据，掌握resample的艺术将对任何希望从分析中提取有意义见解的人来说都是必不可少的。