终身机器学习的知识转移:一个系统的文献综述
Pouya Khodaee1, Herna L Viktor1, Wojtek Michalowski2
1School of Electrical Engineering and Computer Science (EECS), University of Ottawa, 800 King Edward Avenue, Ottawa, ON K1N 6N5 Canada.
概括
终身机器学习 (LML) 使用先前的知识来完成新任务. 本综述探讨了LML中的知识传输 (KT) 技术,发现参数隔离和混合方法主导神经网络模型.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 终身机器学习 (LML) 涉及连续的任务,利用过去的知识应对新的挑战.
- 知识转移 (KT) 对于有效适应和理解LML至关重要.
- 现有研究需要对当前的知识技术及其应用进行整合的概述.
研究的目的:
- 系统地审查终身机器学习 (LML) 中最先进的知识转移 (KT) 技术.
- 分析常用于LML研究的评估指标和数据集.
- 确定LML的趋势和未来的研究方向.
主要方法:
- 来自四个数据库的417篇文章的系统文献综述.
- 精选30篇高度相关的文章进行详细分析.
- 将 KT 技术分为重复,规范化,参数隔离和混合的类别.
主要成果:
- 参数隔离和混合技术主要用于神经网络 (NN) 框架中的 KT.
- 在NN模型中的监督学习是研究的最常见的LML方法.
- 分析涵盖了NN和非NN框架,突出了技术优势.
结论:
- 大多数LML研究重点是使用NNs监督学习.
- 未来的研究机会包括探索Replay KT和超越计算机视觉 (CV) 的应用程序的非NN模型.
- 本综述为LML研究社区提供了全面的更新.
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