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

80
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...
80
Outliers and Influential Points01:08

Outliers and Influential Points

4.0K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
4.0K
Types of Selection01:46

Types of Selection

40.4K
Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
40.4K
Frequency-dependent Selection01:21

Frequency-dependent Selection

22.0K
When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
22.0K

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

AKI-twinX: explainable organ structured digital twin for sepsis AKI trajectory forecasting.

medRxiv : the preprint server for health sciences·2026
Same author

CauReL: Dynamic Counterfactual Learning for Precision Drug Repurposing in Alzheimer's Disease.

Research square·2026
Same author

SpaIM: single-cell spatial transcriptomics imputation via style transfer.

Nature communications·2025
Same author

SpaIM: Single-cell Spatial Transcriptomics Imputation via Style Transfer.

bioRxiv : the preprint server for biology·2025
Same author

PINet: Privileged Information Improve the Interpretablity and generalization of structural MRI in Alzheimer's Disease.

ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine·2024
Same author

ARDaC Common Data Model Facilitates Data Dissemination and Enables Data Commons for Modern Clinical Studies.

Studies in health technology and informatics·2024
查看所有相关文章

相关实验视频

Updated: Jun 27, 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

无监督机器学习的功能选择.

Huyunting Huang1, Ziyang Tang1, Tonglin Zhang1

  • 1Purdue University West Lafayette, Indiana.

IEEE International Conference on Smart Cloud
|May 6, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了无监督机器学习 (ML) 集群的逐步特征选择方法,与使用所有特征相比,提高了高斯混合模型 (GMM) 和k-means的准确性和效率.

关键词:
高斯混合物模型模型的高斯混合物模型.调整后的兰德指数这意味着k-means.一步一步地走了一步.

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

6.8K
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

696

相关实验视频

Last Updated: Jun 27, 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
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
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

696

科学领域:

  • 计算机科学 计算机科学
  • 机器学习 机器学习
  • 数据挖掘 数据挖掘

背景情况:

  • 与监督机器学习相比,无监督机器学习 (ML) 的特征选择较少.
  • 像高斯混合模型 (GMM) 和k-means这样的集群算法通常利用所有可用的功能,可能会影响性能.
  • 在无监督学习中需要改进特征选择技术,以提高聚类的准确性和效率.

研究的目的:

  • 为无监督集群方法提出一个逐步的特征选择方法.
  • 通过特征子集选择来调整和改进高斯混合模型 (GMM) 和k-means算法.
  • 通过模拟和现实世界的数据,对拟议方法与现有方法的性能进行评估.

主要方法:

  • 为集群开发了一个逐步的特征选择策略.
  • 拟议的方法为GMM和k-means选择一个最佳的特征子集.
  • 高斯混合模型 (GMM) 和k-means被潜在地修改,并改进了初始化.

主要成果:

  • 与使用所有功能相比,提出的特征选择方法显示出更高的准确性和计算效率.
  • 使用蒙特卡洛模拟的实验验证实了该方法的有效性.
  • 一个真实世界的数据集分析证实了模拟中的发现,突出了实际应用.

结论:

  • 开发的逐步特征选择方法提高了GMM和k-means集群的性能.
  • 选择相关特征可以提高无监督ML中的计算效率和准确性.
  • 这些发现表明,将GMM和k-means应用于复杂数据集的实际改进.