感应转移
范围(计算机科学)
领域(数学分析)
计算机科学
传输(计算)
学习迁移
知识转移
机器学习
主动学习(机器学习)
知识管理
机器人学习
人工智能
数学
并行计算
程序设计语言
数学分析
机器人
移动机器人
作者
Santisudha Panigrahi,Anuja Nanda,Tripti Swarnkar
出处
期刊:Smart innovation, systems and technologies
日期:2020-10-30
卷期号:: 781-789
被引量:339
标识
DOI:10.1007/978-981-15-5971-6_83
摘要
To facilitate learning in a target domain, transfer learning borrows knowledge from a source domain. What and how to transfer are two main issues that need to be addressed in transferring learning. Different transfer learning algorithms result in different knowledge transferred between them for a couple of domains. To find the optimal transfer learning algorithm that maximizes learning efficiency in the target domain, scientists need to investigate all current computationally intractable transfer learning algorithms exhaustively. A sub-optimal algorithm is selected as a trade-off, which in an ad hoc way requires considerable expertise. In instructional psychology, meanwhile, it is commonly recognized that people enhance the transfer of teaching abilities to decide what to transfer. This paper discusses what is transfer learning, the different transfer learning techniques, future scope, and applications of it.
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