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

Classification of Systems-I01:26

Classification of Systems-I

188
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:
188
Classification of Systems-II01:31

Classification of Systems-II

149
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,
149
Aggregates Classification01:29

Aggregates Classification

327
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...
327
Classification of Signals01:30

Classification of Signals

471
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...
471
Force Classification01:22

Force Classification

1.2K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.2K
Machines: Problem Solving I01:22

Machines: Problem Solving I

331
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
331

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

Strain-induced crumpling of graphene oxide lamellas to achieve fast and selective transport of H<sub>2</sub> and CO<sub>2</sub>.

Nature nanotechnology·2025
Same author

Linguacodus: a synergistic framework for transformative code generation in machine learning pipelines.

PeerJ. Computer science·2024
Same author

Symbolic expression generation <i>via</i> variational auto-encoder.

PeerJ. Computer science·2023
Same author

Code4ML: a large-scale dataset of annotated Machine Learning code.

PeerJ. Computer science·2023
Same author

NFAD: fixing anomaly detection using normalizing flows.

PeerJ. Computer science·2021
Same author

SANgo: a storage infrastructure simulator with reinforcement learning support.

PeerJ. Computer science·2021
Same journal

DARUMA: a gateway to fast and easy prediction of intrinsically disordered regions.

PeerJ. Computer science·2026
Same journal

Alzheimer's disease detection using a quantum deep neural network with Haralick feature extraction and simulated annealing optimization.

PeerJ. Computer science·2026
Same journal

Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network.

PeerJ. Computer science·2026
Same journal

An anomaly detection model for multivariate time series with anomaly perception.

PeerJ. Computer science·2026
Same journal

Retraction: A wormhole attack detection method for tactical wireless sensor networks.

PeerJ. Computer science·2026
Same journal

Evaluation of mental disorder with prioritization of its type by utilizing the bipolar complex fuzzy decision-making approach based on Schweizer-Sklar prioritized aggregation operators.

PeerJ. Computer science·2026
查看所有相关文章

相关实验视频

Updated: Jul 8, 2025

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

机器学习代码片段语义分类

Valeriy Berezovskiy1, Anastasia Gorodilova1, Ekaterina Trofimova1

  • 1HSE University, Moscow, Russia.

PeerJ. Computer science
|December 11, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种使用CodeBERT的自动化方法,用于从Code4ML库中分类机器学习代码片段. 这种方法提高了数据的质量和数量,大大改善了模型培训.

关键词:
代码注释 代码注释编码分类 编码分类 编码分类

更多相关视频

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.1K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K

相关实验视频

Last Updated: Jul 8, 2025

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: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.1K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K

科学领域:

  • 机器学习 机器学习
  • 软件工程 软件工程 软件工程
  • 数据科学数据科学数据科学

背景情况:

  • 程序代码越来越多地用于数据科学模型.
  • 标注代码片段对于模型训练至关重要.
  • 代码4ML集团有限的标记数据 (~0.2%).

研究的目的:

  • 开发一种自动化方法来对Code4ML集中的代码片段进行分类.
  • 为了解决机器学习代码的高质量标记数据的稀缺问题.
  • 为了提高在代码上训练的数据科学模型的性能.

主要方法:

  • 利用基于变压器的模型CodeBERT进行代码片段分类.
  • 开发一个专门的算法,以分离含糊不清的代码片段与多个标签.
  • 使用数据增强策略来扩展标记的数据集.

主要成果:

  • 在代码片段分类方面获得了约89%的F1测试分数.
  • 在Code4ML库中显著增加了标记数据的数量和质量.
  • 证明了CodeBERT在代码分类任务中的增强实用性.

结论:

  • 拟议的方法有效地对机器学习代码片段进行分类.
  • 自动化数据增强提高了监督模型训练.
  • 丰富Code4ML等代码数据集对于推进数据科学模型至关重要.