改进的支持向量机分解方法

Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003 Pub Date : 2003-09-27 DOI:10.1109/ICCIMA.2003.1238096

Weida Zhou, Li Zhang, L. Jiao

{"title":"改进的支持向量机分解方法","authors":"Weida Zhou, Li Zhang, L. Jiao","doi":"10.1109/ICCIMA.2003.1238096","DOIUrl":null,"url":null,"abstract":"An improved decomposition method for SVMs is presented. In our method, we propose a rule for selecting the working set to improve the training speed. The selection rule can be applied to any decomposition methods for SVMs. Simulation results show the feasibility and validity of our algorithm.","PeriodicalId":385362,"journal":{"name":"Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Improved decomposition method for support vector machines\",\"authors\":\"Weida Zhou, Li Zhang, L. Jiao\",\"doi\":\"10.1109/ICCIMA.2003.1238096\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An improved decomposition method for SVMs is presented. In our method, we propose a rule for selecting the working set to improve the training speed. The selection rule can be applied to any decomposition methods for SVMs. Simulation results show the feasibility and validity of our algorithm.\",\"PeriodicalId\":385362,\"journal\":{\"name\":\"Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCIMA.2003.1238096\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCIMA.2003.1238096","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

提出了一种改进的支持向量机分解方法。在我们的方法中，我们提出了一个选择工作集的规则，以提高训练速度。该选择规则可应用于支持向量机的任何分解方法。仿真结果表明了该算法的可行性和有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Improved decomposition method for support vector machines

An improved decomposition method for SVMs is presented. In our method, we propose a rule for selecting the working set to improve the training speed. The selection rule can be applied to any decomposition methods for SVMs. Simulation results show the feasibility and validity of our 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

Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003

自引率

0.00%

发文量