迁移学习研究进展

Survey on Transfer Learning Research

近年来,迁移学习已经引起了广泛的关注和研究.迁移学习是运用已存有的知识对不同但相关领域问题进行求解的一种新的机器学习方法.它放宽了传统机器学习中的两个基本假设:(1)用于学习的训练样本与新的测试样本满足独立同分布的条件;(2)必须有足够可利用的训练样本才能学习得到一个好的分类模型.目的是迁移已有的知识来解决目标领域中仅有少量有标签样本数据甚至没有的学习问题.对迁移学习算法的研究以及相关理论研究的进展进行了综述,并介绍了在该领域所做的研究工作,特别是利用生成模型在概念层面建立迁移学习模型.最后介绍了迁移学习在文本分类、协同过滤等方面的应用工作,并指出了迁移学习下一步可能的研究方向.

In recent years, transfer learning has provoked vast amount of attention and research. Transfer learning is a new machine learning method that applies the knowledge from related but different domains to target domains. It relaxes the two basic assumptions in traditional machine learning： （1） the training （also referred as source domain） and test data （also referred target domain） follow the independent and identically distributed （i.i.d.） condition; （2） there are enough labeled samples to learn a good classification model, aiming to solve the problems that there are few or even not any labeled data in target domains. This paper surveys the research progress of transfer learning and introduces its own works, especially the ones in building transfer learning models by applying generative model on the concept level. Finally, the paper introduces the applications of transfer learning, such as text classification and collaborative filtering, and further suggests the future research direction of transfer learning.