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相关概念视频

Probability Histograms01:17

Probability Histograms

11.1K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
11.1K
Probability Distributions01:32

Probability Distributions

6.8K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
6.8K
Unusual Results01:16

Unusual Results

3.2K
Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
According to the range rule of thumb, any value above or below two standard deviations, 2σ  from the mean, μ  is considered unusual.
Maximum unusual value =...
3.2K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.1K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.1K
Applications of Normal Distribution01:22

Applications of Normal Distribution

5.0K
The normal distribution is a useful statistical tool. One of its practical applications is determining the door height after considering the normal distribution of heights of persons, such that many can pass through it easily without striking their heads. The normal distribution can also determine the probability of a person having a height less than a specific height.
The heights of 15 to 18-year-old males from Chile from 1984 to 1985 followed a normal distribution. The mean height is 172.36...
5.0K
z Scores and Area Under the Curve01:17

z Scores and Area Under the Curve

10.4K
z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of...
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相关实验视频

Updated: Jun 18, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

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一种新的基于概率密度函数的基于可能性的聚类方法,及其用于检测异常元素的应用.

Hung Tran-Nam1,2, Thao Nguyen-Trang1,2, Ha Che-Ngoc3

  • 1Laboratory for Applied and Industrial Mathematics, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City, Vietnam.

Scientific reports
|August 1, 2024
PubMed
概括

本研究引入了一种新的概率密度函数 (PDF) 的可能性集群方法,以检测大数据中的异常. 该方法在基准数据上实现了100%的准确性,在图像数据上实现了高性能,超过了现有的算法.

关键词:
检测异常的情况.图像提取 图像提取有可能的集群聚类.概率密度函数是一个概率密度函数.没有监督的学习学习.

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

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相关实验视频

Last Updated: Jun 18, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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科学领域:

  • 机器学习 机器学习
  • 数据挖掘 数据挖掘
  • 模式识别 模式识别

背景情况:

  • 无监督学习在异常检测方面表现出色,但现有的方法主要处理离散数据,忽视概率密度函数 (PDF).
  • 聚类PDF对于分析复杂数据集至关重要,但当前的技术在识别这些函数中的异常元素方面存在局限性.

研究的目的:

  • 开发一种新的可能性集群算法,用于在概率密度函数中识别异常元素.
  • 通过分析潜在的概率密度函数,实现大数据中有效的异常检测.

主要方法:

  • 通过密度函数提取数据,然后提出一个可能的聚类算法.
  • 创建一个可能的分区,并建立一个决定规则来识别异常.
  • 与基线聚类算法进行比较,例如k-means,FCF和PDF的自动更新聚类.

主要成果:

  • 拟议的算法在模拟的基准数据上实现了100%的准确性,超过了基线方法.
  • 对图像数据的应用产生了G-平均分数从96%到100%,具有灵敏度 (92-100%) 和特异性 (100%).
  • 与现有的算法相比,在聚类PDF文件和检测异常元素方面表现出卓越的性能.

结论:

  • 开发的可能性学方法有效地聚合了概率密度函数并检测了异常.
  • 这种方法通过PDF提供了一个强大的工具,可以通过PDF了解大数据中的内部结构.
  • 该算法显示了在数字时代对先进数据分析的研究和应用的巨大潜力.