Name
Abstract | ||
|---|---|---|
Semisupervised incremental learning is the task of classifying data streams with partially labeled data when annotation information is difficult to obtain. Besides the sequential learning manner and lack of label information, multiple novel classes and concept drift may emerge from incremental learning. Most previous studies have only considered these problems in part. To tackle challenges involve... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/LSP.2018.2843281 | IEEE Signal Processing Letters |
Keywords | Field | DocType |
Neural networks,Feature extraction,Data models,Matrix decomposition,Support vector machines,Clustering methods,Adaptation models | Data modeling,Data stream mining,Pattern recognition,Support vector machine,Feature extraction,Concept drift,Artificial intelligence,Cluster analysis,Artificial neural network,Sequence learning,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
25 | 7 | 1070-9908 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zilin Zhang | 1 | 0 | 0.68 |
| Jun Guo | 2 | 7 | 3.24 |
| Zhang Zhengwen | 3 | 17 | 3.17 |
| Cheng Jin | 4 | 0 | 1.01 |
| Meiguo Gao | 5 | 0 | 1.01 |