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相关概念视频

Weighted Mean00:57

Weighted Mean

4.8K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
4.8K
Mean From a Frequency Distribution01:11

Mean From a Frequency Distribution

15.7K
Sometimes, data gathered from an experiment on a large sample or population are organized into concise tables. In such cases, the frequency of the quantitative data set is plotted in the form of a table. Or else, the data values are grouped into the quantity’s intervals, which form classes, and their respective frequencies are known. That is, the data values are distributed over different categories or classes. This is known as frequency distribution.
When such a data set is encountered,...
15.7K
Regression Toward the Mean01:52

Regression Toward the Mean

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.2K
Skewness01:06

Skewness

10.5K
The measures of central tendency calculated from a data set may not reveal much about its intrinsic distribution. If a plot is made of the data set’s values, the mean and the median may not only differ, but also the plot may have more values on one side of the central tendencies. Such a data set is said to be skewed towards that side.
The longer the tail of the plot on one side, the more skewed it is. The skewness of a data set’s values suggests that the measures of central tendency...
10.5K
Cluster Sampling Method01:20

Cluster Sampling Method

11.5K
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...
11.5K
Trimmed Mean01:10

Trimmed Mean

2.8K
While measuring the mean of a data set, care needs to be taken when associating the mean to its central tendency. The same goes for the arithmetic mean, the geometric mean, or the harmonic mean. This is because the presence of a single outlier data value can significantly affect the mean. That is, the mean is sensitive to fluctuations in the data set.
Although certain measures of central tendency are not sensitive to outliers, there are alternative versions of the mean that get around the...
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相关实验视频

Updated: May 13, 2025

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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可解释的逆代平均值转移网络用于聚类任务.

Bingjie Zhang1, Zihan Yu2, Jian Wang3

  • 1School of Mathematics and Statistics, Weifang University, Weifang, 261061, China.

Neural networks : the official journal of the International Neural Network Society
|May 11, 2025
PubMed
概括

我们介绍了平均转移网络 (MS-Net),这是一个新的可解释的深度学习架构,结合了神经网络和平均转移算法. 对于机器学习任务,MS-Net提供了强大的特征表示,并增强了对机器学习任务的解释性.

关键词:
基于曲率的方法方法.可解释的人工智能可解释的神经网络反向代网络网络的反向代网络平均班次网络 平均班次网络

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相关实验视频

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Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ
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科学领域:

  • 机器学习 机器学习
  • 人工智能的人工智能
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 神经网络擅长特征表示,但缺乏可解释性.
  • 平均转移算法提供了可解释性,但具有有限的表示能力.

研究的目的:

  • 开发一种新的架构,即平均转移网络 (MS-Net),它结合了神经网络的优势和平均转移算法.
  • 为了提高深度学习模型的解释性,同时保持强大的特征表示能力.

主要方法:

  • 提出MS-Net,一个反的模糊聚类网络,每个层都可以解释.
  • 引入了一个以高斯式灵感为灵感的内核,可持续差异化,用于成员层,以确保融合.
  • 开发了一个加权版本 (WMS-Net),以考虑培训的重要性.
  • 考虑使用基于曲率的扩展 (CB-MS-Net,CB-WMS-Net).

主要成果:

  • MS-Net及其变体显示出强大的特征表示,具有固有的可解释性.
  • 提出了理论结果,证明了弱收和强收.
  • 在11个数据集 (5个集群,6个现实世界) 上的模拟结果证实了拟议的算法的有效性.

结论:

  • 在深度学习中,MS-Net提供了一种有希望的方法来弥合可解释性和表示权力之间的差距.
  • 拟议的架构及其扩展是有效的集群任务和现实世界的应用程序.