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

Purposive Learning01:22

Purposive Learning

108
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
108

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

Direct Tissue Mass Spectrometry Imaging by Atmospheric Pressure UV-Laser Desorption Plasma Postionization.

Journal of the American Society for Mass Spectrometry·2020
Same author

A novel secretagogin/ATF4 pathway is involved in oxidized LDL-induced endoplasmic reticulum stress and islet β-cell apoptosis.

Acta biochimica et biophysica Sinica·2020
Same author

Intravenous Thrombolysis before Thrombectomy may Increase the Incidence of Intracranial Hemorrhage inTreating Carotid T Occlusion.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association·2020
Same author

Safety and efficacy of traditional Chinese medicine, Qiaoshao formula, combined with dapoxetine in the treatment of premature ejaculation: An open-label, real-life, retrospective multicentre study in Chinese men.

Andrologia·2020
Same author

New insights into the interplay between miRNAs and autophagy in the aging of intervertebral discs.

Ageing research reviews·2020
Same author

Characterization of a Plasmid-Encoded Resistance-Nodulation-Division Efflux Pump in <i>Klebsiella pneumoniae</i> and <i>Klebsiella quasipneumoniae</i> from Patients in China.

Antimicrobial agents and chemotherapy·2020
Same journal

Kolmogorov-Arnold Guided Local-Global Attention for Medical Image Classification.

Journal of imaging informatics in medicine·2026
Same journal

Artificial Intelligence-Assisted Inner Ear Computed Tomography Analysis: Radiomics-Based Comparison of Affected and Unaffected Ears in Idiopathic Sudden Sensorineural Hearing Loss.

Journal of imaging informatics in medicine·2026
Same journal

High Adoption, Higher Expectations: A Cross-Sectional Survey of Radiologist Engagement with Artificial Intelligence in the United Arab Emirates.

Journal of imaging informatics in medicine·2026
Same journal

Complex-valued Multi-scale Hybrid Attention Network for Fast MRI via Sparsified Data Learning.

Journal of imaging informatics in medicine·2026
Same journal

Automatic Phase and Sequence Identification in Gd-EOB-DTPA-Enhanced Liver MRI Using Deep Convolutional and Sequential Learning.

Journal of imaging informatics in medicine·2026
Same journal

Ultrasound-Based AI in Predicting Hormone Receptor Status in Breast Cancer: Is "Digital Biopsy" Possible.

Journal of imaging informatics in medicine·2026
查看所有相关文章

相关实验视频

Updated: Jun 20, 2025

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

6.6K

在腰部半监督分类上积极标记样本选择的政策学习.

Jinjin Hai1, Jian Chen1, Kai Qiao1

  • 1Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China.

Journal of imaging informatics in medicine
|July 17, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新型的半监督主动学习方法 (RL-based SSAL),有效地选择信息医疗数据进行注释. 这种方法显著提高了模型性能,即使具有有限的标记数据.

关键词:
积极学习是指积极学习.政策学习政策学习奖励函数是一个奖励函数.半监督学习 半监督学习

更多相关视频

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.0K

相关实验视频

Last Updated: Jun 20, 2025

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

6.6K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.0K

科学领域:

  • 医疗成像医学成像
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 由于注释成本,获取标记的医疗数据是具有挑战性的.
  • 半监督学习利用未标记的数据,但对标记的数据质量敏感.
  • 积极学习通过选择信息样本来提高模型性能.

研究的目的:

  • 提出一个统一的半监督主动学习架构 (基于RL的SSAL).
  • 为了提高医疗图像分析模型的效率和性能,使用有限的标记数据.

主要方法:

  • 开发了一种基于强化学习的方法,用于积极的样本选择.
  • 设计了一个新的奖励功能,将预测信心和不确定性结合起来.
  • 交替训练一个半监督网络并进行积极的样本选择.

主要成果:

  • 与半监督基线相比,基于RL的SSAL实现了超过3%的性能改善.
  • 该模型在其他积极学习方法中表现出优越性.
  • 仅使用200个标记样本实现了89.32%的准确性,相当于使用完整的数据集.

结论:

  • 拟议的基于RL的SSAL有效地减少了对广泛标记医疗数据的需求.
  • 强化学习在半监督学习中提供了一个强有力的信息样本选择策略.
  • 这种方法显著推进了机器学习在医学诊断中的应用.