迁移学习研究进展

被引:595
作者
庄福振
罗平
何清
史忠植
机构
[1] 中国科学院智能信息处理重点实验室(中国科学院计算技术研究所)
关键词
迁移学习; 相关领域; 独立同分布; 生成模型; 概念学习;
D O I
10.13328/j.cnki.jos.004631
中图分类号
TP181 [自动推理、机器学习];
学科分类号
摘要
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.
引用
收藏
页码:26 / 39
页数:14
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