Jove
Visualize
联系我们

相关概念视频

Cluster Sampling Method01:20

Cluster Sampling Method

15.4K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
15.4K
Classification of Systems-II01:31

Classification of Systems-II

544
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,
544
Classification of Signals01:30

Classification of Signals

1.5K
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...
1.5K
Classification of Systems-I01:26

Classification of Systems-I

649
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:
649
Aggregates Classification01:29

Aggregates Classification

1.1K
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...
1.1K
Random Variables01:09

Random Variables

18.4K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
18.4K

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 journal

Genome misassembly detection using Stash: A data structure based on stochastic tile hashing.

PloS one·2026
Same journal

Germline-mediated ubiquitous recombination in ScxCre male mice: Implications for tendon research.

PloS one·2026
Same journal

Why do Canadians host refugees? A sequential explanatory mixed-methods study protocol.

PloS one·2026
Same journal

Patient safety management activities partially mediate nursing competences and patient safety culture in Vietnam.

PloS one·2026
Same journal

Instruments that measure evidence-based practice knowledge, skills, and attitudes among health professions students: A systematic review protocol.

PloS one·2026
Same journal

A dual-stream deep learning architecture for business impact scoring and alert escalation.

PloS one·2026
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: Mar 13, 2026

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

8.1K

基于亚空间的随机集体分类器用于高维数据 使用SPARK

Venkaiah Chowdary Bhimineni1, Rajiv Senapati1

  • 1Department of CSE, SRM University, AP, Amaravati, Mangalagiri, Andhra Pradesh, India.

PloS one
|March 11, 2026
PubMed
概括

本研究引入了一种改进的基于子空间的集体分类器 (ISSBEC),以应对高维数据分类的挑战. 这种新的方法提高了处理稀疏,高维数据集的机器学习模型的准确性和稳定性.

科学领域:

  • 机器学习 机器学习
  • 数据科学数据科学数据科学
  • 计算机科学 计算机科学

背景情况:

  • 高维数据分类面临诸如稀疏性和过度适配等挑战,原因是"维度的诅咒".
  • 越来越多的特征导致数据稀疏,阻碍了分类泛化,增加了计算成本,降低了准确性.
  • 现有的方法在具有众多特征的数据集上的可扩展性和性能方面扎.

研究的目的:

  • 提出一种新的集体分类器,旨在克服高维数据分类的局限性.
  • 提高机器学习模型在高维设置中的准确性,稳定性和可扩展性.
  • 引入一个有效处理特征选择和融合的框架,以提高分类性能.

主要方法:

  • 使用min-max规范化的数据规范化.
  • 在主节点上通过改进的深度模糊集群 (IDFC) 进行数据分区.
  • 使用支持矢量机器修改的递归特征消除 (SVM-MRFE) 和在奴隶节点上的特征融合进行特征选择.
  • 一个改进的基于子空间的集体分类器 (ISSBEC),集成基于特征融合的随机子空间 (FF-RSS),混合空间增强 (MSE) 和多个基础分类器.

主要成果:

  • 与最先进的方法相比,拟议的ISSBEC分类器表现出更好的准确性和稳定性.

更多相关视频

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

1.4K

相关实验视频

Last Updated: Mar 13, 2026

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

8.1K
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

1.4K
  • 实验结果验证了拟议方法在处理高维数据集方面的有效性.
  • 该框架为涉及大型特征集的机器学习任务提供了可扩展的解决方案.
  • 结论:

    • ISSBEC分类器为高维数据分类挑战提供了有效的解决方案.
    • 拟议的特征选择,融合和组合分类方法显著提高了模型性能.
    • 基于Spark的实现确保了现实世界的应用程序的可扩展性和效率.