摘要
传统机器学习方法假设训练数据和测试数据分布一致,但在许多实际应用中这个假设并不能得到满足.针对该情况,文中提出了一种非参数化的迁移学习算法———多步桥接精化算法.首先构造一系列中间模型来建立不同领域之间的桥梁,然后在近邻的模型间进行标签传播,实现从源领域到目标领域的判别信息迁移.实验结果表明,分布相近的模型使迁移变得平滑,并使精化结果不敏感于初始标签,文中算法在分类精度上优于其他对比算法.
In the traditional machine learning methods,it is assumed that the training and test data have an identical distribution.However,this assumption is not valid in many cases.In order to solve this problem,a non-parametric transfer learning algorithm named Multi-Step Bridged Refinement is proposed.In this algorithm,a series of intermediate models is constructed to bridge different domains,and the label propagation between neighboring mo-dels is performed,through which the discriminative information is transferred from the source domain into the target one.Experimental results show that the models with similar distribution contribute to smooth transfer and make the refinement results insensitive to the initial label,and that the proposed algorithm attains a classification accuracy higher than that from other algorithms.
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2011年第5期108-114,共7页
Journal of South China University of Technology(Natural Science Edition)
基金
广东省自然科学基金资助项目(9451064101003233)
广东省科技攻关项目(2007B010200044)
华南理工大学中央高校基本科研业务费资助项目(2009ZM0125
2009ZM0189)
关键词
迁移学习
标签传播
文本分类
交互精化
transfer learning
label propagation
text classification
mutual refinement
