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

87
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...
87
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

56
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...
56
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

43
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
43

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

A Case of Familial Chylomicronemia Syndrome Caused by a Novel Homozygous GPIHBP1 Mutation Successfully Treated with the Selective PPARα Modulator Pemafibrate.

Journal of atherosclerosis and thrombosis·2026
Same author

Efficacy of Automated Urinalysis in Detecting Low-Level Bacteriuria and Discriminating Bacterial Gram Status.

Clinical laboratory·2026
Same author

Defining a Cardiogenic Shock Center and Its Relationship to Outcomes Among Patients With Cardiogenic Shock　- A Scoping Review.

Circulation reports·2026
Same author

Effectiveness of the Shock Team on Short-Term Outcomes in Patients With Cardiogenic Shock　- Systematic Review and Meta-Analysis.

Circulation reports·2026
Same author

Comparative Efficacy of Noradrenaline vs. Other Vasopressors on Outcomes in Patients With Cardiogenic Shock　- A Systematic Review and Meta-Analysis.

Circulation reports·2025
Same author

The Association Between Body Mass Index and In-hospital Mortality in Patients with Acute Myocardial Infarction Undergoing Percutaneous Coronary Intervention.

International heart journal·2025
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: Jul 8, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.5K

通过安全的模式修剪,为预测模式采矿模型进行有效的模型选择.

Takumi Yoshida1, Hiroyuki Hanada2, Kazuya Nakagawa1

  • 1Department of Engineering, Nagoya Institute of Technology, Nagoya, Aichi 466-8555, Japan.

Patterns (New York, N.Y.)
|December 18, 2023
PubMed
概括
此摘要是机器生成的。

预测模式挖掘从结构化数据中构建模型,但面临着太多模式的挑战. 本研究介绍了一种安全的图案修剪方法,以有效地管理模型构建中的图案数.

关键词:
凸凸的优化优化图表采矿是指采矿的采矿方式.项目集 采矿 采矿 采矿 采矿预测模式 采矿 采矿的预测模式安全的选安全的选序列采矿是采矿的一种方式.稀疏的学习稀疏的学习

更多相关视频

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

7.6K
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

6.8K

相关实验视频

Last Updated: Jul 8, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.5K
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

7.6K
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

6.8K

科学领域:

  • 数据挖掘和机器学习
  • 计算智能是一种计算智能.
  • 模式识别 模式识别

背景情况:

  • 预测模式挖掘从结构化数据 (如集合,图表和序列) 中构建模型.
  • 它利用子结构 (模式) 作为模型特征,面临着指数式模式增长的挑战.
  • 这种增长使模型构建复杂化,并降低了效率.

研究的目的:

  • 提出一种新的方法来应对在预测型模式挖矿中过多的模式数量的挑战.
  • 引入安全的图案修剪方法,以实现高效的模型构建.
  • 证明该方法在各种结构化数据类型和机器学习任务中的适用性.

主要方法:

  • 开发了一种"安全的模式修剪"技术,以减轻模式的组合式爆炸.
  • 将剪裁方法集成到预测模式采矿工作流中.
  • 通过使用集合,图形和序列数据,对回归和分类任务进行数值实验.

主要成果:

  • 提出的安全模式修剪方法有效地减少了模式的数量.
  • 该方法在实际数据分析和模型构建中表现出效率.
  • 在各种结构化数据的回归和分类问题中成功应用.

结论:

  • 安全的模式修剪是预测模式采矿中模式爆炸问题的可行解决方案.
  • 该方法提高了从结构化数据构建预测模型的实用性和效率.
  • 这种方法为处理复杂数据类型的机器学习提供了重大进步.