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

Gauss's Law: Planar Symmetry01:27

Gauss's Law: Planar Symmetry

8.0K
A planar symmetry of charge density is obtained when charges are uniformly spread over a large flat surface. In planar symmetry, all points in a plane parallel to the plane of charge are identical with respect to the charges. Suppose the plane of the charge distribution is the xy-plane, and the electric field at a space point P with coordinates (x, y, z) is to be determined. Since the charge density is the same at all (x, y) - coordinates in the z = 0 plane, by symmetry, the electric field at P...
8.0K
Gauss's Law01:07

Gauss's Law

7.4K
If a closed surface does not have any charge inside where an electric field line can terminate, then the electric field line entering the surface at one point must necessarily exit at some other point of the surface. Therefore, if a closed surface does not have any charges inside the enclosed volume, then the electric flux through the surface is zero. What happens to the electric flux if there are some charges inside the enclosed volume? Gauss's law gives a quantitative answer to this question.
7.4K
Gauss's Law: Problem-Solving01:10

Gauss's Law: Problem-Solving

1.8K
Gauss's law helps determine electric fields even though the law is not directly about electric fields but electric flux. In situations with certain symmetries (spherical, cylindrical, or planar) in the charge distribution, the electric field can be deduced based on the knowledge of the electric flux. In these systems, we can find a Gaussian surface S over which the electric field has a constant magnitude. Furthermore, suppose the electric field is parallel (or antiparallel) to the area...
1.8K
Poisson Probability Distribution01:09

Poisson Probability Distribution

8.3K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
8.3K
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.6K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.6K
Gauss's Law: Spherical Symmetry01:26

Gauss's Law: Spherical Symmetry

7.6K
A charge distribution has spherical symmetry if the density of charge depends only on the distance from a point in space and not on the direction. In other words, if the system is rotated, it doesn't look different. For instance, if a sphere of radius R is uniformly charged with charge density ρ0, then the distribution has spherical symmetry. On the other hand, if a sphere of radius R is charged so that the top half of the sphere has a uniform charge density ρ1 and the bottom half...
7.6K

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 systematic review of machine learning on clinical MALDI-TOF MS.

Briefings in bioinformatics·2026
Same author

Probabilistic day-ahead forecasting of system-level renewable energy and electricity demand.

Nature communications·2026
Same author

Addressing wide-data studies of gene expression microarrays with the Relevance Feature and Vector Machine.

Computers in biology and medicine·2025
Same author

Automated web-based typing of Clostridioides difficile ribotypes via MALDI-TOF MS.

BMC bioinformatics·2025
Same author

Predictive model of ibuprofen treatment failure in very preterm infants with patent ductus arteriosus using machine learning techniques.

Journal of perinatology : official journal of the California Perinatal Association·2025
Same author

Bayesian learning of feature spaces for multitask regression.

Neural networks : the official journal of the International Neural Network Society·2024
Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
Same journal

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Survey on Human-Centric Voice-Face Multimodal Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026
查看所有相关文章

相关实验视频

Updated: Jul 23, 2025

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
14:58

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

Published on: June 2, 2010

9.6K

适应性稀疏高斯过程

Vanessa Gomez-Verdejo, Emilio Parrado-Hernandez, Manel Martinez-Ramon

    IEEE transactions on neural networks and learning systems
    |July 19, 2023
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了适应性稀疏高斯过程 (GP) 对于非静止环境. 这种新的方法有效地更新了具有遗忘因子的模型,使机器智能的快速,低成本的在线学习成为可能.

    更多相关视频

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
    14:27

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

    Published on: June 26, 2013

    15.7K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    442

    相关实验视频

    Last Updated: Jul 23, 2025

    Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
    14:58

    Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

    Published on: June 2, 2010

    9.6K
    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
    14:27

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

    Published on: June 26, 2013

    15.7K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    442

    科学领域:

    • 机器学习 机器学习
    • 人工智能的人工智能
    • 统计建模 统计建模

    背景情况:

    • 适应性学习对于非静止环境至关重要,它要求机器忘记过时的数据分布.
    • 高效的算法需要紧的,计算上便宜的模型更新来进行在线参数调整.
    • 目前的解决方案不足以应对这些适应性学习挑战.

    研究的目的:

    • 提出第一个适应性稀疏高斯过程 (GP),解决计算效率和非静止性问题.
    • 开发一种以最小的计算成本进行紧模型更新的方法.
    • 在动态环境中实现有效的在线参数更新.

    主要方法:

    • 重构了一个变量稀疏GP (VSGP) 算法,其中包含了适应性的忘记因子.
    • 开发了一种新方法,只更新一个单一的诱导点和每个新数据样本的模型参数.
    • 专注于简化模型推理以实现高效的在线处理.

    主要成果:

    • 拟议的算法在推断过程中显示出快速的趋同.
    • 通过单个推理代实现了高效的模型更新,即使在高度非静止的设置中也是如此.
    • 在预测后平均值和置信区间估计方面表现强.

    结论:

    • 适应性稀疏GP通过忘记过去的数据,有效地处理非静止环境.
    • 该方法通过紧的,单次代模型更新提供计算效率.
    • 在建模预测后期和信心区间方面表现优于最先进的方法.