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

Reducing Line Loss01:18

Reducing Line Loss

180
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
180
Classification of Signals01:30

Classification of Signals

557
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...
557
Aggregates Classification01:29

Aggregates Classification

353
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...
353
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.5K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.5K
Classification of Systems-I01:26

Classification of Systems-I

223
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:
223
Multiple Regression01:25

Multiple Regression

3.1K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.1K

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

SPMFE-UNet: shape perception and multi-scale features enhancement UNet for robust abdominal organ and skin lesion segmentation.

Biomedical physics & engineering express·2026
Same author

SS-DTI: A deep learning method integrating semantic and structural information for drug-target interaction prediction.

Journal of bioinformatics and computational biology·2025
Same author

Deeply integrating latent consistent representations in high-noise multi-omics data for cancer subtyping.

Briefings in bioinformatics·2024
Same author

TsImpute: an accurate two-step imputation method for single-cell RNA-seq data.

Bioinformatics (Oxford, England)·2023
Same author

A protein succinylation sites prediction method based on the hybrid architecture of LSTM network and CNN.

Journal of bioinformatics and computational biology·2022
Same author

Confidence intervals for the common odds ratio based on the inverse sinh transformation.

Journal of biopharmaceutical statistics·2021
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jul 29, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

588

深度分类与线性增强的逻辑到软max函数.

Hao Shao1, Shunfang Wang2,3

  • 1School of Mathematics and Statistics, Yunnan Unverisity, Kunming 650504, China.

Entropy (Basel, Switzerland)
|May 27, 2023
PubMed
概括
此摘要是机器生成的。

对于卷积神经网络 (CNN) 来说,Orthogonal-Softmax是一种新的损失函数,通过提高特征可区分性来增强深度分类. 这种方法促进了类内紧性和类间差异,以更好地识别图像.

关键词:
格拉姆·施密特正角化在正交的软max.这是分类分类的分类.卷积神经网络是一种卷积神经网络.

更多相关视频

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
04:57

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Published on: May 16, 2022

16.0K

相关实验视频

Last Updated: Jul 29, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

588
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
04:57

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Published on: May 16, 2022

16.0K

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 包括图像识别和目标检测在内的深度分类任务正在迅速推进.
  • 卷积神经网络 (CNN) 是这些进步的核心,软max是性能的关键组成部分.
  • 现有的软max方法在特征区分和类别分离方面可能存在局限性.

研究的目的:

  • 引入Orthogonal-Softmax,一个新的,直观的学习目标功能,用于深度分类任务.
  • 为了增强由CNNs提取的特征的歧视力.
  • 为了同时提高类内紧性和类间差异性.

主要方法:

  • 开发了Orthogonal-Softmax,这是一个基于Gram-Schmidt正交的新型损失函数,用于线性近似.
  • 使用直角多项式扩展,与传统和泰勒-软max相比,建立更强的关系.
  • 设计一个线性软max损失以优化类别分离和特征紧性.

主要成果:

  • 与传统和泰勒-软max相比,通过直角多项式扩展证明了更强的关系.
  • 拟议的损失函数实际上获得了对分类具有高度歧视性的特征.
  • 在四个基准数据集上的实验验验证了提出的方法在促进类内紧性和类间差异方面的有效性.

结论:

  • 正角-软max提供了一个有前途的方法来提高深度分类性能在CNNs.
  • 该方法成功地提高了特征的可区分性,从而导致更好的分类结果.
  • 未来的工作可能会探索Orthogonal-Softmax对非地面真实样本的应用.