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

Aggregates Classification01:29

Aggregates Classification

326
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...
326
Classification of Systems-I01:26

Classification of Systems-I

186
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:
186
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

28.5K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
28.5K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

32.8K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
32.8K
Classification of Systems-II01:31

Classification of Systems-II

146
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,
146
Classification of Signals01:30

Classification of Signals

462
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...
462

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: New advances in embryo development and embryo-endometrial interface.

Frontiers in endocrinology·2026
Same author

ExposoGraph: An Interactive Platform for Carcinogen Bioactivation and Detoxification Pathway Visualization.

Medical oncology (Northwood, London, England)·2026
Same author

Tryptophan metabolism in embryo implantation and decidualization.

Tissue & cell·2026
Same author

ExposoGraph: An Interactive Platform for Carcinogen Bioactivation and Detoxification Pathway Visualization.

bioRxiv : the preprint server for biology·2026
Same author

Innate-like T Cell Biology in the Tumor Microenvironment Implications for Cancer Immunotherapy.

Cells·2026
Same author

Proteomics discovery of MTDH and SND1 interaction vulnerabilities in ovarian cancer.

Scientific reports·2025

相关实验视频

Updated: Jul 5, 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

AlphaML:一个清晰,可读,可解释,透明和阐明的二进制分类平台,用于表格数据.

Ahmad Nasimian1,2,3, Saleena Younus1,2,3, Özge Tatli1,2,3

  • 1Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Lund, Sweden.

Patterns (New York, N.Y.)
|January 24, 2024
PubMed
概括
此摘要是机器生成的。

我们开发了alphaML,这是一个用户友好的平台,用于透明和可解释的二进制分类模型. 它提供了广泛的定制和强大的评估,使机器学习可以在不需要编码的情况下使用.

关键词:
在 TabNet TabNet 中使用.在XGBoost上使用.深度表格式学习 (deep tabular learning) 是一种深度表格式学习.药物敏感性预测预测组合学习组合学习可以解释的人工智能AI功能选择 功能选择超参数优化超参数优化机器学习是机器学习.精准医学是一门精准医学.

更多相关视频

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.0K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K

相关实验视频

Last Updated: Jul 5, 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 Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.0K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K

科学领域:

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

背景情况:

  • 二进制分类对于机器学习具有广泛应用的关键.
  • 现有的平台往往缺乏透明度,可解释性和用户友好性.
  • 需要可访问的工具,提供清晰和可解释的模型.

研究的目的:

  • 介绍 alphaML,这是一个创建清晰,可读,可解释,透明和阐明 (CLETE) 二元分类模型的新平台.
  • 提供一个用户友好的界面,不需要编程专业知识.
  • 提供全面的定制和强大的模型评估.

主要方法:

  • 集成了15个具有全球和本地解释能力的机器学习算法.
  • 包括特征选择,超参数搜索,采样和规范化方法.
  • 开发了用于超参数调整的自定义指标,并使用NegLog2RMSL进行模型评估.

主要成果:

  • AlphaML提供了透明和可解释的二进制分类模型.
  • 该平台提供了广泛的定制选项和图形界面.
  • 在各种数据集上进行测试,alphaML在各种表格数据配置中展示了多功能性.

结论:

  • AlphaML成功地解决了对用户友好,透明和可解释的二进制分类工具的需求.
  • 该平台的设计和功能使得更广泛的受众可以使用先进的机器学习.
  • 阿尔法ML在各种数据科学领域的应用方面显示出显著的前景.