Name
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/SERA.2018.8477228 | 2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA) |
Keywords | Field | DocType |
Support Tensor Machine,Tensor Decomposition,Classification Algorithm | Data mining,Tensor,Computer science,Matrix decomposition,Support vector machine,Stress (mechanics),Artificial intelligence,Statistical classification,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-5386-5887-1 | 0 | 0.34 |
References | Authors | |
21 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Xiang | 1 | 21 | 4.73 |
| Qian Jiang | 2 | 11 | 3.86 |
| Jing He | 3 | 93 | 14.67 |
| Xin Jin | 4 | 0 | 0.34 |
| Liwen Wu | 5 | 0 | 2.70 |
| Shaowen Yao | 6 | 86 | 26.85 |