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| 基于k个标记样本的弱监督学习框架 |
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付治1,2, 王红军1,2, 李天瑞1,2, 滕飞1,2, 张继1,2
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1.西南交通大学 信息科学与技术学院, 四川 成都 611756;2.综合交通大数据应用技术国家工程实验室(西南交通大学), 四川 成都 611756
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| 摘要: |
| 聚类是机器学习领域中的一个研究热点，弱监督学习是半监督学习中一个重要的研究方向，有广泛的应用场景.在对聚类与弱监督学习的研究中，提出了一种基于k个标记样本的弱监督学习框架.该框架首先用聚类及聚类置信度实现了标记样本的扩展.其次，对受限玻尔兹曼机的能量函数进行改进，提出了基于k个标记样本的受限玻尔兹曼机学习模型.最后，完成了对该模型的推理并设计相关算法.为了完成对该框架和模型的检验，选择公开的数据集进行对比实验，实验结果表明，基于k个标记样本的弱监督学习框架实验效果较好. |
| 关键词: 机器学习 弱监督学习 聚类 |
| DOI：10.13328/j.cnki.jos.005919 |
| 分类号:TP181 |
| 基金项目:四川省国际科技创新合作重点项目（2019YFH0097） |
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| Weakly Supervised Learning Framework Based on k Labeled Samples |
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FU Zhi1,2, WANG Hong-Jun1,2, LI Tian-Rui1,2, TENG Fei1,2, ZHANG Ji1,2
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1.School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China;2.National Engineering Laboratory of Integrated Transportation Big Data Application Technology(Southwest Jiaotong University), Chengdu 611756, China
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| Abstract: |
| Clustering is an active research topic in the field of machine learning. Weakly supervised learning is an important research direction in semi-supervised learning, which has wide range of application scenarios. In the research of clustering and weakly supervised learning, it is proposed that a framework of weakly supervised learning is based on k labeled samples. Firstly, the framework expands labeled samples by clustering and clustering confidence level. Secondly, the energy function of the restricted Boltzmann machine is improved, and a learning model of the restricted Boltzmann machine based on k labeled samples is proposed. Finally, the model of ratiocination and algorithm are proposed. In order to test the framework and the model, a series of public data sets are chosen for comparative experiments. The experimental results show that the proposed weakly supervised learning framework based on k labeled samples is more effective. |
| Key words: machine learning weakly supervised learning clustering model |
