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A self-organizing incremental neural network for continual supervised learning
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AI
DOI：10.1016/j.eswa.2021.115662.png)
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摘要
En 中文
Continual learning algorithms can adapt to changes of data distributions, new classes, and even completely new tasks without catastrophically forgetting previously acquired knowledge. Here, we present a novel self-organizing incremental neural network, GSOINN+, for continual supervised learning. GSOINN+ learns a topological mapping of the input data to an undirected network and uses a weighted nearest-neighbor rule with fractional distance for classification. GSOINN+ learns incrementally-new classification tasks do not need to be specified a priori, and no rehearsal of previously learned tasks with stored training sets is required. In a series of sequential learning experiments, we show that GSOINN+ can mitigate catastrophic forgetting, even when completely new tasks are to be learned.
Keyword:
Catastrophic forgetting
Concept drift
Continual learning
Incremental learning
Supervised learning
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期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
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An enhanced self-organizing incremental neural network for online unsupervised learning.png)
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