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ICANN
2003
Springer

Meta-learning for Fast Incremental Learning

10 years 9 months ago
Meta-learning for Fast Incremental Learning
Model based learning systems usually face to a problem of forgetting as a result of the incremental learning of new instances. Normally, the systems have to re-learn past instances to avoid this problem. However, the re-learning process wastes substantial learning time. To reduce learning time, we propose a novel incremental learning system, which consists of two neural networks: a main-learning module and a meta-learning module. The main-learning module approximates a continuous function between input and desired output value, while the meta-learning module predicts an appropriate change in parameters of the main-learning module for incremental learning. The meta-learning module acquires the learning strategy for modifying current parameters not only to adjust the main-learning module’s behavior for new instances but also to avoid forgetting past learned skills.
Takayuki Oohira, Koichiro Yamauchi, Takashi Omori
Added 06 Jul 2010
Updated 06 Jul 2010
Type Conference
Year 2003
Where ICANN
Authors Takayuki Oohira, Koichiro Yamauchi, Takashi Omori
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