论文标题
一项基于计算情报的转移学习的调查
A Survey on Computational Intelligence-based Transfer Learning
论文作者
论文摘要
转移学习（TL）的目标是提供一个框架，以利用获得的知识从源到目标数据。与传统的机器学习方法相比，转移学习方法能够从当前领域对更好的数据模式进行建模。但是，香草TL需要使用基于计算智能的TL来提高性能。本文研究了基于计算情报的转移学习技术，并将其分类为基于神经网络的，基于进化算法的基于群体智能和基于模糊逻辑的转移学习。
The goal of transfer learning (TL) is providing a framework for exploiting acquired knowledge from source to target data. Transfer learning approaches compared to traditional machine learning approaches are capable of modeling better data patterns from the current domain. However, vanilla TL needs performance improvements by using computational intelligence-based TL. This paper studies computational intelligence-based transfer learning techniques and categorizes them into neural network-based, evolutionary algorithm-based, swarm intelligence-based and fuzzy logic-based transfer learning.
