基于k个标记样本的弱监督学习框架  

Weakly Supervised Learning Framework Based on k Labeled Samples

作　　者：付治 王红军[1,2] 李天瑞[1,2] 滕飞[1,2] 张继[1,2] FU Zhi;WANG Hong-Jun;LI Tian-Rui;TENG Fei;ZHANG Ji(School of Information Science and Technology,Southwest Jiaotong University,Chengdu 611756,China;National Engineering Laboratory of Integrated Transportation Big Data Application Technology(Southwest Jiaotong University),Chengdu 611756,China)

机构地区：[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

出　　处：《软件学报》2020年第4期981-990,共10页Journal of Software

基　　金：四川省国际科技创新合作重点项目(2019YFH0097)。

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.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.

关 键 词：机器学习 弱监督学习 聚类 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]