Review on meta-learning
Deep learning and reinforcement learning are limited by small sample data set, which is impossible to realize the strong generalization learning ability.Meta-learning can make up for their shortcomings effectively.The values formed by accumulated experience feedback the corresponding signals to prom...
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| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
| Published: |
Beijing Xintong Media Co., Ltd
2021-01-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2021009/ |
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| Summary: | Deep learning and reinforcement learning are limited by small sample data set, which is impossible to realize the strong generalization learning ability.Meta-learning can make up for their shortcomings effectively.The values formed by accumulated experience feedback the corresponding signals to promote the model to adjust itself.It allows the artificial intelligence to learn to complete complex tasks quickly, which implements true artificial intelligence.Firstly, the basic principles of meta-learning were outlined.Secondly, according to the different forms of meta-knowledge, the research status of various methods was analyzed in depth.Finally, the application potential and the future development trends of meta-learning was discussed . |
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| ISSN: | 1000-0801 |