Learning with Feature and Distribution Evolvable Streams

By Zhenyu Zhang, Peng Zhao, Yuan Jiang, Zhi-Hua Zhou. 2020

49 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Jingwen Ye
2. Xinchao Wang
3. Jie Song
4. Yifang Fu
5. Peng Zhao
6. Xingyi Yang
7. Mingli Song
8. Bo-Jian Hou

Citations in the corpus

• Learning with Recoverable Forgetting (2022, positive): “In addition to the above works that tend to forbid the catastrophic forgetting of the old tasks, some researchers [19,64,18,17] pay more attention on the decremental cases where some features may vanish while feature evolving.”
• Storage Fit Learning with Feature Evolvable Streams (2020, background): “Over the last several years, feature evolvable learning has drawn extensive attentions (Zhang et al. 2016; Hou, Zhang, and Zhou 2017a; Hou and Zhou 2018; Ye et al. 2018; Zhang et al. 2020), where old features will vanish and new features will emerge when data streams come continuously or in an onlin”