Jove
Visualize
联系我们
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

相关概念视频

Survival Tree01:19

Survival Tree

61
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...
61
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

1.5K
Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
1.5K
Improving Translational Accuracy02:07

Improving Translational Accuracy

9.1K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
9.1K
Machines: Problem Solving II01:30

Machines: Problem Solving II

296
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
296
Machines: Problem Solving I01:22

Machines: Problem Solving I

300
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
300
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

41
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
41

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

Pitfalls and risks of generative AI in machine learning.

Patterns (New York, N.Y.)·2026
Same author

Reassessing feature-based Android malware detection in a contemporary context.

PloS one·2026
Same author

REFORMS: Consensus-based Recommendations for Machine-learning-based Science.

Science advances·2024
Same author

Evaluation of Recurrent Neural Network Models for Parkinson's Disease Classification Using Drawing Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2021
Same author

Significant cognitive decline in Parkinson's disease exacerbates the reliance on visual feedback during upper limb reaches.

Neuropsychologia·2021
Same author

Machine learning discriminates a movement disorder in a zebrafish model of Parkinson's disease.

Disease models & mechanisms·2020
Same journal

Zero-shot reconstruction of mutant spatial transcriptomes.

Patterns (New York, N.Y.)·2026
Same journal

Dendritic nonlinearities mitigate communication costs.

Patterns (New York, N.Y.)·2026
Same journal

Erratum: Agentic AI as a coordination paradigm in digital health and agri-food systems.

Patterns (New York, N.Y.)·2026
Same journal

Spacing effect improves generalization in biological and artificial systems.

Patterns (New York, N.Y.)·2026
Same journal

A multi-modal foundation model for brain disease diagnosis and medical imaging.

Patterns (New York, N.Y.)·2026
Same journal

DuoMod-Net: Logarithmic balancing and geometric refinement for imbalanced semi-supervised medical image segmentation.

Patterns (New York, N.Y.)·2026
查看所有相关文章

相关实验视频

Updated: Jun 7, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K

避免常见的机器学习陷

Michael A Lones1

  • 1School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, UK.

Patterns (New York, N.Y.)
|November 21, 2024
PubMed
概括
此摘要是机器生成的。

常见的机器学习 (ML) 错误破坏了研究的信心. 本教程指导用户避免ML实践,模型构建,评估,比较和报告可靠的学术发现的陷.

关键词:
这是指导的指导,指导的指导.机器学习是机器学习.在实践中,实践实践.

更多相关视频

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K

相关实验视频

Last Updated: Jun 7, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 数据科学数据科学数据科学

背景情况:

  • 机器学习 (ML) 实践容易出现错误,可能会破坏对ML衍生结果和产品的信任.
  • 使用ML的学术研究需要严格的比较和有效的结论,这些领域往往容易犯常见错误.

研究的目的:

  • 概述机器学习实践中经常出现的错误.
  • 提供有关在ML生命周期中避免这些错误的指导.
  • 提高机器学习应用在学术研究中的可靠性和有效性.

主要方法:

  • 该教程涵盖了机器学习过程的五个关键阶段.
  • 模型前的建筑考虑因素.
  • 可靠的模型构建技术.
  • 强大的模型评估策略.
  • 公平的模型比较方法.
  • 有效的结果报告标准.

主要成果:

  • 在ML工作流程中识别常见的错误.
  • 用于减少模型构建和评估中的错误的策略.
  • 确保公平的模型比较的指南.
  • 对ML结果的透明和准确报告的最佳实践.

结论:

  • 避免常见的错误对于保持对机器学习的信心至关重要.
  • 实施概述的实践可以提高学术ML研究的严谨性和有效性.
  • 本教程是为研究人员改善他们的ML方法的实用指南.