支撑张量机的发展

2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2018-06-01 DOI:10.1109/SERA.2018.8477228

Yi Xiang, Qian Jiang, Jing He, Xin Jin, Liwen Wu, Shaowen Yao

{"title":"支撑张量机的发展","authors":"Yi Xiang, Qian Jiang, Jing He, Xin Jin, Liwen Wu, Shaowen Yao","doi":"10.1109/SERA.2018.8477228","DOIUrl":null,"url":null,"abstract":"In recent years, tensor-based machine learning methods, in which the Support Tensor Machine (STM) is a typical technology, have gradually attracted the attention of researchers. Compared with Support Vector Machine (SVM), STM has superior generalization ability that can make full use of the structural information of data. However, it still faces many challenges due to the imperfection of its theoretical basis and model. In order to study the further development of STM, this paper provides a survey about the potential and existing problems in STM.","PeriodicalId":161568,"journal":{"name":"2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"The Advance of Support Tensor Machine\",\"authors\":\"Yi Xiang, Qian Jiang, Jing He, Xin Jin, Liwen Wu, Shaowen Yao\",\"doi\":\"10.1109/SERA.2018.8477228\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent years, tensor-based machine learning methods, in which the Support Tensor Machine (STM) is a typical technology, have gradually attracted the attention of researchers. Compared with Support Vector Machine (SVM), STM has superior generalization ability that can make full use of the structural information of data. However, it still faces many challenges due to the imperfection of its theoretical basis and model. In order to study the further development of STM, this paper provides a survey about the potential and existing problems in STM.\",\"PeriodicalId\":161568,\"journal\":{\"name\":\"2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA)\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SERA.2018.8477228\",\"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 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SERA.2018.8477228","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 5

摘要

近年来，以支持张量机(Support Tensor machine, STM)为代表的基于张量的机器学习方法逐渐受到研究人员的关注。与支持向量机(SVM)相比，STM具有更强的泛化能力，可以充分利用数据的结构信息。然而，由于其理论基础和模型的不完善，它仍然面临着许多挑战。为了研究STM的进一步发展，本文对STM的潜力和存在的问题进行了综述。

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

The Advance of Support Tensor Machine

In recent years, tensor-based machine learning methods, in which the Support Tensor Machine (STM) is a typical technology, have gradually attracted the attention of researchers. Compared with Support Vector Machine (SVM), STM has superior generalization ability that can make full use of the structural information of data. However, it still faces many challenges due to the imperfection of its theoretical basis and model. In order to study the further development of STM, this paper provides a survey about the potential and existing problems in STM.

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

2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA)

自引率

0.00%

发文量