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相关概念视频

The Representativeness Heuristic02:13

The Representativeness Heuristic

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The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Variance01:15

Variance

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 The deviations show how spread out the data are about the mean. A positive deviation occurs when the data value exceeds the mean, whereas a negative deviation occurs when the data value is less than the mean. If the deviations are added, the sum is always zero. So one cannot simply add the deviations to get the data spread. By squaring the deviations, the numbers are made positive; thus, their sum will also be positive.
The standard deviation measures the spread in the same units as the...
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Convenience Sampling Method00:55

Convenience Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
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State Space Representation01:27

State Space Representation

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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
228
Systematic Sampling Method01:17

Systematic Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...
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相关实验视频

Updated: Jul 15, 2025

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

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基于多重组的稀疏表示,用于论挖掘.

Zohre Karimi1

  • 1School of Engineering, Damghan University, Damghan, Iran. z.karimi@du.ac.ir.

Scientific reports
|September 23, 2023
PubMed
概括

这项研究通过开发一种新的基于稀疏多元体的用户评论表示方式来增强论挖掘. 这种方法显著提高了对大型数据集的情绪分析的分类准确性.

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 数据挖掘 数据挖掘

背景情况:

  • 消费者对产品/服务的看法是重要的商业指标.
  • 传统的文本特征采集意见挖掘方法面临着高维度和噪音等挑战.
  • 现有的非线性特征选择方法使用近邻图,可以包括不同的极性.

研究的目的:

  • 为了增强意见采矿的特征代表性.
  • 为了解决经典文本特征表示方法的局限性.
  • 提出一种新的方法,结合多重假设和散的财产来代表意见.

主要方法:

  • 提出了一个新的算法,利用多重假设和稀疏属性作为先前知识.
  • 它学习了基于此前知识的用户评论的图形表示.
  • 学习图的光谱属性被用来创建一个新的特征空间.

主要成果:

  • 拟议的算法在IMDB和亚马逊审查数据集上进行了测试.
  • 与最先进的方法相比,它在F测量和精度方面取得了显著的改进.
  • 使用线性SVM分类器实现了99.15% (IMDB) 和91.97% (亚马逊) 的最高准确度.

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结论:

  • 稀疏的多元化代表性导致了论挖掘的重大进展.
  • 该方法有效地学习了内在的数据结构,克服了以前技术的局限性.
  • 实验结果验证了拟议方法及其基础假设的有效性.