gaussian

简明释义

英['ɡaʊsɪən]美['gaʊsiən]

adj. 高斯的

英英释义

单词用法

同义词

反义词

例句

1.A gaussian 高斯 kernel is commonly used in support vector machines for classification tasks.

在支持向量机分类任务中，通常使用高斯核。

2.The gaussian 高斯 distribution is fundamental in statistics for representing real-valued random variables.

在统计学中，高斯分布是表示实值随机变量的基础。

3.In image processing, a gaussian 高斯 blur is often used to reduce noise and detail.

在图像处理中，高斯模糊常用于减少噪声和细节。

4.The gaussian 高斯 noise in the signal can be filtered out using advanced algorithms.

信号中的高斯噪声可以通过先进的算法进行滤波。

5.We applied a gaussian 高斯 function to model the data points in our experiment.

我们应用了一个高斯函数来建模实验中的数据点。

作文

In the field of statistics and probability theory, the term gaussian refers to a specific type of distribution known as the normal distribution. This distribution is characterized by its bell-shaped curve, which is symmetric around the mean. The concept of gaussian distributions is fundamental in many areas of science and engineering, as it describes how values are dispersed in a population. For example, when measuring heights in a large group of people, the results will often form a gaussian distribution, where most individuals are close to the average height, and fewer individuals are at the extremes of very tall or very short. The mathematical foundation of a gaussian distribution is defined by its mean and standard deviation. The mean indicates the center of the distribution, while the standard deviation measures the spread of the data around the mean. When we talk about a gaussian distribution, we often refer to the empirical rule, which states that approximately 68% of the data falls within one standard deviation from the mean, about 95% falls within two standard deviations, and about 99.7% falls within three standard deviations. This property makes the gaussian distribution particularly useful for predicting outcomes and understanding variability in data.Beyond statistics, the concept of gaussian has applications in various fields such as physics, finance, and machine learning. In physics, many natural phenomena can be modeled using gaussian functions, such as the distribution of particle velocities in a gas. In finance, asset returns often exhibit gaussian characteristics, which helps in risk assessment and portfolio management. In machine learning, algorithms often assume that the data follows a gaussian distribution, allowing for more straightforward analysis and predictions.Moreover, the influence of the gaussian function extends to image processing and computer vision. The gaussian filter is commonly used to reduce noise and detail in images, enhancing the overall quality of visual data. By applying a gaussian blur, one can achieve smoother transitions and emphasize important features within an image.In conclusion, the term gaussian encompasses a wide range of applications and significance across various disciplines. Understanding the properties of gaussian distributions allows researchers and practitioners to make informed decisions based on statistical data. Whether it is analyzing human characteristics, predicting financial trends, or improving image quality, the gaussian concept remains a cornerstone of modern scientific inquiry. Therefore, grasping the essence of gaussian distributions is essential for anyone looking to delve deeper into the world of data analysis and interpretation.

在统计学和概率论领域，术语gaussian指的是一种特定类型的分布，称为正态分布。这种分布的特点是其钟形曲线，在均值周围对称。gaussian分布的概念在科学和工程的许多领域中是基础，因为它描述了一个人群中数值的分散情况。例如，当测量一大组人的身高时，结果通常会形成gaussian分布，其中大多数个体接近平均身高，而极端的非常高或非常矮的个体则较少。gaussian分布的数学基础由其均值和标准差定义。均值表示分布的中心，而标准差则衡量数据围绕均值的分布程度。当我们谈论gaussian分布时，通常会提到经验法则，该法则指出，大约68%的数据落在均值的一个标准差内，大约95%的数据落在两个标准差内，而大约99.7%的数据落在三个标准差内。这个特性使得gaussian分布在预测结果和理解数据变异性方面特别有用。除了统计学，gaussian的概念在物理、金融和机器学习等多个领域都有应用。在物理学中，许多自然现象可以使用gaussian函数建模，例如气体中粒子速度的分布。在金融领域，资产收益通常表现出gaussian特征，这有助于风险评估和投资组合管理。在机器学习中，算法通常假设数据遵循gaussian分布，从而便于分析和预测。此外，gaussian函数的影响还扩展到图像处理和计算机视觉。gaussian滤波器常用于减少图像中的噪声和细节，提高视觉数据的整体质量。通过应用gaussian模糊，可以实现更平滑的过渡，并强调图像中的重要特征。总之，术语gaussian涵盖了各个学科中广泛的应用和重要性。理解gaussian分布的性质使研究人员和从业者能够基于统计数据做出明智的决策。无论是分析人类特征、预测金融趋势，还是改善图像质量，gaussian概念仍然是现代科学研究的基石。因此，掌握gaussian分布的本质对于任何希望深入数据分析和解释的人来说都是至关重要的。