基于偏二叉树算法的最小二乘双支持向量机

Qing Yu, R. Liu
{"title":"基于偏二叉树算法的最小二乘双支持向量机","authors":"Qing Yu, R. Liu","doi":"10.1109/CISP-BMEI.2018.8633117","DOIUrl":null,"url":null,"abstract":"Based on the classic least squares twin support vector machine (LSTSVM), an efficient but simple Least Squares Twin Support Vector Machine-Partial Binary Tree (LSTSVM-PBT)for binary classification problem was proposed. This algorithm introduces binary tree into LSTSVM, the problem summed up as binary tree classification for each data ultimately. Compared to traditional SVM, LSTSVM-PBT has low time complexity. Reliable theoretical analysis and extensive experiments show that LSTBSVM-PBT is fast computationally and obtain the higher performance than traditional algorithm.","PeriodicalId":117227,"journal":{"name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Least Squares Twin SVM Based on Partial Binary Tree Algorithm\",\"authors\":\"Qing Yu, R. Liu\",\"doi\":\"10.1109/CISP-BMEI.2018.8633117\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on the classic least squares twin support vector machine (LSTSVM), an efficient but simple Least Squares Twin Support Vector Machine-Partial Binary Tree (LSTSVM-PBT)for binary classification problem was proposed. This algorithm introduces binary tree into LSTSVM, the problem summed up as binary tree classification for each data ultimately. Compared to traditional SVM, LSTSVM-PBT has low time complexity. Reliable theoretical analysis and extensive experiments show that LSTBSVM-PBT is fast computationally and obtain the higher performance than traditional algorithm.\",\"PeriodicalId\":117227,\"journal\":{\"name\":\"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CISP-BMEI.2018.8633117\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CISP-BMEI.2018.8633117","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

在经典最小二乘双支持向量机(LSTSVM)的基础上,提出了一种高效、简单的二值分类问题的最小二乘双支持向量机-部分二叉树(LSTSVM- pbt)算法。该算法将二叉树引入LSTSVM,最终将问题归结为对每个数据进行二叉树分类。与传统支持向量机相比,LSTSVM-PBT具有较低的时间复杂度。可靠的理论分析和大量的实验表明,LSTBSVM-PBT算法计算速度快,性能优于传统算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Least Squares Twin SVM Based on Partial Binary Tree Algorithm
Based on the classic least squares twin support vector machine (LSTSVM), an efficient but simple Least Squares Twin Support Vector Machine-Partial Binary Tree (LSTSVM-PBT)for binary classification problem was proposed. This algorithm introduces binary tree into LSTSVM, the problem summed up as binary tree classification for each data ultimately. Compared to traditional SVM, LSTSVM-PBT has low time complexity. Reliable theoretical analysis and extensive experiments show that LSTBSVM-PBT is fast computationally and obtain the higher performance than traditional algorithm.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信