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.



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.



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