基于k个标记样本的弱监督学习框架

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.

Weakly Supervised Learning Framework Based on k Labeled Samples

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, we choose a series of public data sets for comparative experiments. The experimental results show that the proposed weakly supervised learning framework based on k labeled samples is more effective.