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相关概念视频

Probability Histograms01:17

Probability Histograms

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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.
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Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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Classification of Systems-I01:26

Classification of Systems-I

190
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Classification of Systems-II01:31

Classification of Systems-II

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Probability Distributions01:32

Probability Distributions

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 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...
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相关实验视频

Updated: Jul 11, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

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尺寸缩小和特征视觉表示基于条件概率应用到活动分类的条件概率.

Alihuén García-Pavioni1, Beatriz López1

  • 1Exit Grup, University of Girona, Carrer Universitat de Girona, 6, Girona, 17003, Girona, Spain.

Computers in biology and medicine
|November 5, 2023
PubMed
概括
此摘要是机器生成的。

时间序列状态变化表示 (SCRTS) 有效地减少可穿戴传感器数据的时间序列维度. 这种新的特征提取方法实现了高分类准确性,并有助于数据解释.

关键词:
加速计 加速计 加速计活动认可 活动认可条件概率是有条件的概率.缩小尺寸的缩小方式功能提取 功能提取功能 视觉表示 功能 视觉表示长度独立于长度.马尔科夫模型的特点 马尔科夫模型的特点时间序列时间序列时间序列分类时间序列分类时间序列分布时间序列分布.

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相关实验视频

Last Updated: Jul 11, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.9K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Trajectory Data Analyses for Pedestrian Space-time Activity Study

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科学领域:

  • 数据科学数据科学数据科学
  • 机器学习 机器学习
  • 信号处理 信号处理

背景情况:

  • 当代设备产生大量的时间序列数据.
  • 有效的特征提取对于减小维度和信息保存至关重要.
  • 现有的方法在处理可变长度时间序列和解释性方面面临挑战.

研究的目的:

  • 为了引入一种新的时间序列特征提取技术,时间序列状态变化表示 (SCRTS).
  • 开发一种长度独立的方法,在不同的时间序列中产生一致的特征.
  • 为了更好地理解时间序列特征的视觉解释.

主要方法:

  • 开发了时间序列状态变化表示 (SCRTS) 方法.
  • SCRTS使用条件概率 (马尔科夫模型特征) 和价值分布.
  • 该技术产生固定数量的特征,无论输入时间序列的长度如何.

主要成果:

  • SCRTS显著降低了时间序列的维度,例如,从5499值降至31值.
  • 该方法实现了高分类准确性,在最佳情况下达到98%.
  • 视觉表示提供了对不同时间序列的区分特征的见解.

结论:

  • SCRTS是一种有效和高效的时间序列特征提取技术.
  • 该方法提供了显著的维度减少,同时保留了相关信息.
  • 在分类任务中,SCRTS表现出强的表现,并有助于数据的可解释性.