[1]聂飞,高艳丽,邓赵红,等.可能性匹配知识迁移原型聚类算法[J].智能系统学报,2020,15(5):978-989.[doi:10.11992/tis.201810028]
NIE Fei,GAO Yanli,DENG Zhaohong,et al.Possibility-matching based knowledge transfer prototype clustering algorithm[J].CAAI Transactions on Intelligent Systems,2020,15(5):978-989.[doi:10.11992/tis.201810028]
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NIE Fei,GAO Yanli,DENG Zhaohong,et al.Possibility-matching based knowledge transfer prototype clustering algorithm[J].CAAI Transactions on Intelligent Systems,2020,15(5):978-989.[doi:10.11992/tis.201810028]
可能性匹配知识迁移原型聚类算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
15
期数:
2020年第5期
页码:
978-989
栏目:
学术论文—知识工程
出版日期:
2020-09-05
- Title:
- Possibility-matching based knowledge transfer prototype clustering algorithm
- Keywords:
- transfer prototype clustering; transfer learning mechanism; mandatory matching; possibility matching; prototype clustering; adjustability
- 分类号:
- TP181
- 文献标志码:
- A
- 摘要:
- 针对迁移原型聚类的优化问题，本文以模糊知识匹配迁移原型聚类为基础，介绍了聚类场景中从源域到目标域的迁移学习机制，明确了源域聚类中心辅助目标域得到更好的聚类效果。但目前此类迁移机制依然面临如下的挑战：1）如何克服已有迁移原型聚类方法中不同类别间的知识强制性匹配带来的负作用。2）当源域与目标域相似度较低时，如何避免模糊强制性匹配的不合理性以及过于依赖源域知识的缺陷被放大。为此，研究了一种新的迁移原型聚类机制，即可能性匹配知识迁移原型机制，并基于此实现了2个具体的迁移聚类算法。借鉴可能性匹配的思想，该算法可以自动选择和偏重有用的源域知识，克服了源域和目标域之间的强制性匹配限制，具有较好的可调节性。研究结果表明：在不同迁移场景下模拟数据集和真实NG20groups数据集上的实验研究表明，提出的算法较已有的相关算法展现了更好的性能。
- 参考文献/References:
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备注/Memo
收稿日期:2018-10-24。
基金项目:国家自然科学基金面上项目（61170122）
作者简介:聂飞,硕士研究生,主要研究方向为智能计算与模式识别;高艳丽,主要研究方向为不确定性人工智能和计算技术;邓赵红,教授,主要研究方向为不确定性人工智能及其应用。主持国家和省部级项目多项,获得教育部科技进步一等奖。Neuro computing等6个国际期刊编委。发表学术论文100余篇。
通讯作者:邓赵红.E-mail:dengzhaohong@jiangnan.edu.cn
更新日期/Last Update:
2021-01-15