Name
Abstract | ||
|---|---|---|
Multiview datasets are frequently encountered in learning tasks, such as web data mining and multimedia information analysis. Given a multiview dataset, traditional learning algorithms usually decompose it into several single-view datasets, from each of which a single-view model is learned. In contrast, a multiview learning algorithm can achieve better performance by cooperative learning on the multiview data. However, existing multiview approaches mainly focus on the views that are visible and ignore the hidden information behind the visible views, which usually contains some intrinsic information of the multiview data, or vice versa. To address this problem, this paper proposes a multiview fuzzy logic system which utilizes both the hidden information shared by the multiple visible views and the information of each visible view. Extensive experiments were conducted to validate its effectiveness. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TFUZZ.2018.2871005 | IEEE Trans. Fuzzy Systems |
Keywords | Field | DocType |
Fuzzy logic,Brain modeling,Task analysis,Support vector machines,Indexes,Ions,Collaboration | Web data mining,Fuzzy logic system,Task analysis,Multiview learning,Support vector machine,Fuzzy logic,Artificial intelligence,Multimedia information,Cooperative learning,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
abs/1807.08595 | 6 | 1063-6706 |
Citations | PageRank | References |
7 | 0.41 | 21 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Te Zhang | 1 | 12 | 2.14 |
| Zhaohong Deng | 2 | 50 | 4.35 |
| Dongrui Wu | 3 | 1658 | 93.01 |
| Shitong Wang | 4 | 1485 | 109.13 |