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

Trial and Error and Algorithm01:12

Trial and Error and Algorithm

105
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
105
Censoring Survival Data01:09

Censoring Survival Data

72
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
72
Heuristics01:21

Heuristics

83
Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
83
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

115
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
115
Multiple Comparison Tests01:13

Multiple Comparison Tests

3.9K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
3.9K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

45
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
45

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

Progesterone-KISS1 axis impairs amniotic epithelial cell function and promotes premature rupture of membranes.

Placenta·2026
Same author

Case Report: First occurrence of smoldering multiple myeloma in activated phosphoinositide 3-Kinase δ syndrome.

Frontiers in oncology·2026
Same author

The feasibility of ovarian tissue cryopreservation in selected females after hematopoietic stem cell transplantation.

Bone marrow transplantation·2026
Same author

Enhancement of content, purity and anti-myocardial ischemia-reperfusion injury activity of persimmon leaf flavonoids: Ultrasonic-assisted extraction, purification and related mechanism.

Ultrasonics sonochemistry·2026
Same author

High stability double Stokes-Mueller polarimetry under oblique incidence.

Optics express·2026
Same author

Prediction of tumor progression in intermediate and advanced hepatocellular carcinoma undergoing TACE combined with targeted immunotherapy.

Frontiers in oncology·2026
Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026
Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026
Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026
Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026
Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026
Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026
查看所有相关文章

相关实验视频

Updated: Jun 16, 2025

Barnes Maze Testing Strategies with Small and Large Rodent Models
12:59

Barnes Maze Testing Strategies with Small and Large Rodent Models

Published on: February 26, 2014

41.8K

改进"饥饿游戏"的搜索算法,以优化谷歌网模型.

Yanqiu Li1, Shizheng Qu2, Huan Liu1

  • 1School of Data Science and Artificial Intelligence, Jilin Engineering Normal University, Changchun, China.

PloS one
|August 16, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种改进的饥饿游戏搜索 (ATHGS) 算法,用于优化神经网络参数. 该ATHGS-GoogleNet模型实现了高精度 (98.1%),在自适应参数调整方面表现出卓越的性能.

更多相关视频

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.0K
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.0K

相关实验视频

Last Updated: Jun 16, 2025

Barnes Maze Testing Strategies with Small and Large Rodent Models
12:59

Barnes Maze Testing Strategies with Small and Large Rodent Models

Published on: February 26, 2014

41.8K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.0K
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.0K

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 手动调整神经网络参数是耗时且低效的.
  • 手动找到最佳参数组合是非常具有挑战性的.
  • 有效的参数优化对于神经网络性能至关重要.

研究的目的:

  • 开发一种适应性方法来优化神经网络参数.
  • 提出一个改进的饥饿游戏搜索算法 (ATHGS).
  • 引入一个新的ATHGS-GoogleNet模型,以提高性能.

主要方法:

  • 整合了适应性权重和混乱映射到饥饿游戏搜索算法中,以创建ATHGS.
  • 使用了ATHGS算法来优化谷歌网参数.
  • 进行了比较实验,以验证ATHGS算法和ATHGS-GoogleNet模型.

主要成果:

  • 在三种工程设计中,ATHGS算法展示了卓越的优化性能.
  • 在ATHGS-GoogleNet模型的准确性达到98.1%.
  • 该模型具有高灵敏度 (100%) 和精度 (99.5%).

结论:

  • 拟议的ATHGS算法有效地优化了神经网络参数的适应性.
  • ATHGS-GoogleNet模型在准确性,灵敏性和精度方面提供了显著的改进.
  • 这种方法为复杂的神经网络参数调整提供了有效的解决方案.