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相关概念视频

Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.0K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
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Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
8.2K
Uniform Distribution01:19

Uniform Distribution

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The uniform distribution is a continuous probability distribution of events with an equal probability of occurrence. This distribution is rectangular.
Two essential properties of this distribution are
4.8K
Chebyshev's Theorem to Interpret Standard Deviation01:15

Chebyshev's Theorem to Interpret Standard Deviation

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Chebyshev’s theorem, also known as Chebyshev’s Inequality, states that the proportion of values of a dataset for K standard deviation is calculated using the equation:
4.1K
Student t Distribution01:31

Student t Distribution

5.8K
The population standard deviation is rarely known in many day-to-day examples of statistics. When the sample sizes are large, it is easy to estimate the population standard deviation using a confidence interval, which provides results close enough to the original value. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
The Student t distribution was developed by William S. Goset (1876–1937) of the...
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关闭形式的高斯扩散估计小和大支向量的分类.

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    直接马调整 (DGT) 提供了一种快速的分析方法,用于优化支持向量机 (SVM) 中的高斯核扩散. 这种方法显著加快了分类任务,即使是在大型数据集上,与最先进的性能相匹配.

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    科学领域:

    • 机器学习 机器学习
    • 计算科学 计算科学

    背景情况:

    • 具有高斯核的支持向量机 (SVM) 对分类非常强大.
    • 调整内核扩散参数 (gamma) 是至关重要的,但通常是计算上昂贵的.
    • 现有的方法需要代训练,限制大数据集的可扩展性.

    研究的目的:

    • 开发一种直接的,非代的方法来计算最佳的高斯核分布.
    • 显著加快SVM的培训和应用,特别是对于大规模的问题.
    • 提高SVM分类的效率和性能.

    主要方法:

    • 制定一个直接的分析表达式来计算内核扩散.
    • 尽量减少高斯和理想核矩阵之间的差异.
    • 与随机抽样集成,用于处理大型数据集.

    主要成果:

    • 拟议的直接马调 (DGT) 方法的性能与最先进的方法相美.
    • 在小型数据集上,DGT比现有的方法快一到两倍.
    • 在大型数据集 (最多3100万个模式) 上,DGT更快,性能优于线性SVM.

    结论:

    • DGT提供了一个高效和有效的解决方案,用于调整高斯核SVM.
    • 该方法显示了显著的加速和性能改进,特别是在大规模分类方面.
    • DGT为计算密集型代优化技术提供了一个实用的替代方案.