模式识别与人工智能
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模式识别与人工智能  2013, Vol. 26 Issue (5): 432-439    DOI:
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融合迁移学习的TranCo-Training分类模型
唐焕玲1,于立萍1,鲁明羽2
1.山东省高校智能信息处理重点实验室山东工商学院烟台264005
2.大连海事大学信息科学技术学院大连116026
An Enhanced TranCo-Training Categorization Model with Transfer Learning
TANG Huan-Ling1,YU Li-Ping1,LU Ming-Yu2
1. Key Laboratory of Intelligent Information Processing in Universities of Shandong Shandong Institute of Business and Technology,Yantai 264005
2. Information Science and Technology College,Dalian Maritime University,Dalian 116026

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摘要 半监督学习中当未标注样本与标注样本分布不同时,将导致分类器偏离目标数据的主题,降低分类器的正确性.文中采用迁移学习技术,提出一种TranCo-Training分类模型.每次迭代,根据每个未标注样本与其近邻标注样本的分类一致性计算其迁移能力,并根据迁移能力从辅助数据集向目标数据集迁移实例.理论分析表明,辅助样本的迁移能力与其训练错误损失成反比,该方法能将训练错误损失最小化,避免负迁移,从而解决半监督学习中的主题偏离问题.实验表明,TranCo-Training优于随机选择未标注样本的RdCo-Training算法,尤其是给定少量的标注目标样本和大量的辅助未标注样本时.
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唐焕玲
于立萍
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关键词 迁移学习半监督学习协同训练朴素贝叶斯文本分类    
Abstract:When unlabeled data draw from different distributions compared with labeled data in semi-supervise learning,the topic biases the target domain and the performance of semi-supervised classifier decreases. The transfer technique is applied to improve the performance of semi-supervised learning in this paper. An enhanced categorization model called TranCo-training is studied which combines transfer learning techniques with co-training methods. The transferability of each unlabeled instance is computed by an important component of TranCo-training according to the consistency with its labeled neighbors. At each iteration,unlabeled instances are transferred from auxiliary dataset according to their transfer ability. Theoretical analysis indicates that transfer ability of an unlabeled instance is inversely proportional to its training error,which minimizes the training error and avoids negative transfer. Thereby,the problem of topic bias in semi-supervised learning is solved. The experimental results show that TranCo-training algorithm achieves better performance than the RdCo-training algorithm when a few labeled data on target domain and abundant unlabeled data on auxiliary domain are provided.
Key wordsTransfer Learning    Semi-Supervised Learning    Co-Training    Naive Bayesian    Text Categorization   
收稿日期: 2012-11-29     
ZTFLH: TP181  
基金资助:国家自然科学基金资助项目(No.61073133,61175053,61272369,61272244)
作者简介: 唐焕玲,女,1970年生,博士,副教授,主要研究方向为数据挖掘、机器学习.E-mail:thl01@163.com.于立萍,女,1971年生,博士,副教授,主要研究方向为模式识别、计算机视觉、智能控制.鲁明羽(通讯作者),1963年生,教授,博士生导师,主要研究方向为数据挖掘、机器学习.
引用本文:   
唐焕玲,于立萍,鲁明羽. 融合迁移学习的TranCo-Training分类模型[J]. 模式识别与人工智能, 2013, 26(5): 432-439. TANG Huan-Ling,YU Li-Ping,LU Ming-Yu. An Enhanced TranCo-Training Categorization Model with Transfer Learning. , 2013, 26(5): 432-439.
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