[1]王晓初,包芳,王士同,等.基于最小最大概率机的迁移学习分类算法[J].智能系统学报编辑部,2016,11(1):84-92.[doi:10.11992/tis.201505024]
WANG Xiaochu,BAO Fang,WANG Shitong,et al.Transfer learning classification algorithms based on minimax probability machine[J].CAAI Transactions on Intelligent Systems,2016,11(1):84-92.[doi:10.11992/tis.201505024]
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WANG Xiaochu,BAO Fang,WANG Shitong,et al.Transfer learning classification algorithms based on minimax probability machine[J].CAAI Transactions on Intelligent Systems,2016,11(1):84-92.[doi:10.11992/tis.201505024]
基于最小最大概率机的迁移学习分类算法
《智能系统学报》编辑部[ISSN 1673-4785/CN 23-1538/TP] 卷:
11
期数:
2016年第1期
页码:
84-92
栏目:
学术论文—机器学习
出版日期:
2016-02-25
- Title:
- Transfer learning classification algorithms based on minimax probability machine
- Keywords:
- transfer learning; minimax probability machine; classification; source domain; target domain; regularization
- 分类号:
- TP391.4
- 摘要:
- 传统的迁移学习分类算法利用源域中大量有标签的数据和目标域中少量有标签的数据解决相关但不相同目标域的数据分类问题,但对于已知源域的不同类别数据均值的迁移学习分类问题并不适用。为了解决这个问题,利用源域的数据均值和目标域的少量标记数据构造迁移学习约束项,对最小最大概率机进行正则化约束,提出了基于最小最大概率机的迁移学习分类算法,简称TL-MPM。在20 News Groups数据集上的实验结果表明,目标域数据较少时,所提算法具有更高的分类正确率,从而说明了算法的有效性。
- Abstract:
- Traditional transfer learning classification algorithms solve related (but not identical) data classification issues by using a large number of labeled samples in the source domain and small amounts of labeled samples in the target domain. However, this technique does not apply to the transfer learning of data from different categories of learned source domain data. To solve this problem, we constructed a transfer learning constraint term using the source domain data and the limited labeled data in the target domain to generate a regularized constraint for the minimax probability machine. We propose a transfer learning classification algorithm based on the minimax probability machine known as TL-MPM. Experimental results on 20 Newsgroups data sets demonstrate that the proposed algorithm has higher classification accuracy for small amounts of target domain data. Therefore, we confirm the effectiveness of the proposed algorithm.
- 参考文献/References:
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备注/Memo
收稿日期:2015-05-11;改回日期:。
基金项目:国家自然科学基金资助项目(61170122,61272210).
作者简介:王晓初,男,1987年生,硕士研究生,主要研究方向为人工智能与模式识别、数字图像处理;包芳,女,1970年生,教授,博士,主要研究方向为人工智能、模式识别、图像视觉;王士同,男,1964年生,教授、博士生导师,主要研究方向为人工智能。主持或参加过6项国家自然科学基金项目,1项国家教委优秀青年教师基金项目,其他省部级科研项目10多项。先后获国家教委、中船总公司和江苏省省部级科技进步奖5项。发表学术论文近百篇,被SCI、EI检索50余篇。
通讯作者:王晓初.E-mail:icnice@yeah.net.
更新日期/Last Update:
1900-01-01