The Advance of Support Tensor Machine

被引：0
|
作者：
Xiang, Yi [1 ]
He, Jing [1 ]
Wu, LiWen [1 ]
Jiang, Qian [1 ]
Jin, Xin [1 ]
Yao, Shaowen [1 ]
机构：
[1] Yunnan Univ, Sch Software, Kunming, Yunnan, Peoples R China
来源：
2018 IEEE/ACIS 16TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING RESEARCH, MANAGEMENT AND APPLICATION (SERA) | 2018年
关键词：
Support Tensor Machine; Tensor Decomposition; Classification Algorithm; MULTILINEAR DISCRIMINANT-ANALYSIS; IMAGE CLASSIFICATION; RANK; DECOMPOSITIONS; FRAMEWORK;
D O I：
暂无
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
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.
引用
收藏
页码：121 / 128
页数：8
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