Jove
Visualize
联系我们

相关概念视频

Aggregates Classification01:29

Aggregates Classification

305
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...
305
Classification of Systems-I01:26

Classification of Systems-I

176
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:
176
Prediction Intervals01:03

Prediction Intervals

2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.2K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

101
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
101
Survival Tree01:19

Survival Tree

63
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
63
Classification of Signals01:30

Classification of Signals

417
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
417

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Editorial: Passive brain-computer interfaces: moving from lab to real-world application.

Frontiers in computational neuroscience·2026
Same author

Explainable AI for sign language recognition models: Integrating Grad-Cam LIME and Integrated Gradients.

PloS one·2025
Same author

Instantiating the onEEGwaveLAD Framework for Real-Time Muscle Artefact Identification and Mitigation in EEG Signals.

Sensors (Basel, Switzerland)·2025
Same author

PyNoetic: A modular python framework for no-code development of EEG brain-computer interfaces.

PloS one·2025
Same author

Evaluation of EEG pre-processing and source localization in ecological research.

Frontiers in neuroimaging·2025
Same author

onEEGwaveLAD: A fully automated online EEG wavelet-based learning adaptive denoiser for artefacts identification and mitigation.

PloS one·2025
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关实验视频

Updated: Jun 11, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

653

基于时间序列分类器的参数化事件原始的基于规则的全球模型不可知XAI方法.

Ephrem Tibebe Mekonnen1,2, Luca Longo1,2, Pierpaolo Dondio1

  • 1School of Computer Science, College of Health and Science, Technological University Dublin, Dublin, Ireland.

Frontiers in artificial intelligence
|October 7, 2024
PubMed
概括

这项研究引入了一种新的后期可解释的AI方法,用于深度学习时间序列分类器. 该方法生成决策树规则,以揭示关键时间步骤,增强模型的可解释性.

关键词:
可解释的人工智能决策树是一个决策树.深度学习是一种深度学习.模型不可知论者是一个模型不可知论者.这是一个后期的特点.时间序列分类时间序列分类

更多相关视频

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.3K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K

相关实验视频

Last Updated: Jun 11, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

653
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.3K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 时间序列分类至关重要,但经常使用黑子深度学习模型.
  • 现有的可解释AI (XAI) 方法由于其时间性质,很难适应时间序列数据.

研究的目的:

  • 为基于深度学习的时间序列分类器提出一种全新的全球后期可解释的方法.
  • 为了提高复杂的时间序列分类模型的解释性.

主要方法:

  • 培训和评估深度学习时间序列分类器.
  • 提取和聚类参数化的原始事件 (例如,增加,减少,局部极端) 来识别原型事件.
  • 使用这些原型事件作为对训练在模型预测上的决策树分类器的输入.

主要成果:

  • 拟议的方法生成决策树图和规则集作为解释.
  • 对UCR档案数据集的实验表明,全球解释性得到了改进.
  • 使用准确性,忠实性,稳定性,节点数和规则深度等指标进行评估.

结论:

  • 新的全球后期方法有效地提高了深度学习时间序列分类器的可解释性.
  • 决策树规则提取为模型行为提供了可理解的见解.