一般化低级更新:低级培训数据修改的模型参数界限

Hiroyuki Hanada1, Noriaki Hashimoto2, Kouichi Taji3

  • 1Center for Advanced Intelligence Project, RIKEN, Tokyo 103-0027, Japan hiroyuki.hanada@riken.jp.

Neural computation
|October 16, 2023
PubMed
概括

本研究介绍了一种用于增量机器学习 (ML) 的通用低级更新 (GLRU) 方法. 当数据发生变化时,GLRU可以有效地更新模型,从而受益于交叉验证和特征选择.

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