A boosting Self-Training Framework based on Instance Generation with Natural Neighbors for K Nearest Neighbor
Author:
Funder
National Natural Science Foundation of China
the Project of Chongqing Natural Science Foundation
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-020-01732-1.pdf
Reference65 articles.
1. Happy SL, Dantcheva A, Bremond F (2019) A Weakly Supervised learning technique for classifying facial expressions. Pattern Recognition Letters 128(1):162–168
2. Song Y, Upadhyay S, Peng H, Mayhew S, Roth D (2019) Toward any-language zero-shot topic classification of textual documents. Artif Intell 274:33–150
3. Ahmed Ghoneim, Ghulam Muhammad, M. Shamim Hossain, Cervical cancer classification using convolutional neural networks and extreme learning machines, Future Generation Computer Systems 102 (2020) 643–649
4. Abayomi-Alli O, Misra S, Abayomi-Alli A, Odusami M (2019) A review of soft techniques for SMS spam classification: methods, approaches and applications. Eng Appl Artif Intell 86:197–212
5. Adcock CJ, Meade N (2017) Using parametric classification trees for model selection with applications to financial risk management. European Journal of Operational Research 259(2):746–765
Cited by 22 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. A self-training method based on fast binary bare-bones particle swarm optimization for semi-supervised classification;Engineering Applications of Artificial Intelligence;2024-10
2. Semi-supervised regression via embedding space mapping and pseudo-label smearing;Applied Intelligence;2024-07-19
3. Ensemble methods and semi-supervised learning for information fusion: A review and future research directions;Information Fusion;2024-07
4. Weakly supervised glottis segmentation on endoscopic images with point supervision;Biomedical Signal Processing and Control;2024-06
5. Imbalanced Data Classification Based on Improved Random-SMOTE and Feature Standard Deviation;Mathematics;2024-05-30
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3