Classification by Clusters Analysis - An Ensemble Technique in a Semi-supervised Classification

A. Jurek, Y. Bi, Shengli Wu, C. Nugent
{"title":"Classification by Clusters Analysis - An Ensemble Technique in a Semi-supervised Classification","authors":"A. Jurek, Y. Bi, Shengli Wu, C. Nugent","doi":"10.1109/ICTAI.2011.137","DOIUrl":null,"url":null,"abstract":"In this work we adopt a previously introduced meta-learning classification method for semi-supervised learning problems. In our previous work we illustrated that the method is successful when applied in a supervised classification problem. In our current work the results demonstrate that following refinements made to the method it can be successfully applied to semi-supervised classification cases.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"494 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTAI.2011.137","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

In this work we adopt a previously introduced meta-learning classification method for semi-supervised learning problems. In our previous work we illustrated that the method is successful when applied in a supervised classification problem. In our current work the results demonstrate that following refinements made to the method it can be successfully applied to semi-supervised classification cases.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
聚类分析分类——半监督分类中的集成技术
在这项工作中,我们采用了先前介绍的元学习分类方法来解决半监督学习问题。在我们之前的工作中,我们证明了该方法在监督分类问题中是成功的。在我们目前的工作中,结果表明,对该方法进行了以下改进,它可以成功地应用于半监督分类情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Independence-Based MAP for Markov Networks Structure Discovery Flexible, Efficient and Interactive Retrieval for Supporting In-silico Studies of Endobacteria Recurrent Neural Networks for Moisture Content Prediction in Seed Corn Dryer Buildings Top Subspace Synthesizing for Promotional Subspace Mining RELIEF-C: Efficient Feature Selection for Clustering over Noisy Data
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1