计算机科学
宪法
人工智能
领域(数学分析)
线性判别分析
模式识别(心理学)
零(语言学)
机器学习
数学
政治学
语言学
数学分析
哲学
法学
作者
Guihua Wen,Jiajiong Ma,Yang Hu,Huihui Li,Lijun Jiang
标识
DOI:10.1016/j.artmed.2020.101951
摘要
Traditional Chinese Medicine (TCM) considers that the personal constitution determines the occurrence trend and therapeutic effects of certain diseases, which can be recognized by machine learning through tongue images. However, current machine learning methods are confronted with two challenges. First, there are not some larger tongue image databases available. Second, they do not use the domain knowledge of TCM, so that the imbalance of constitution categories cannot be solved. Therefore, this paper proposes a new constitution recognition method based on the zero-shot learning with the knowledge of TCM. To further improve the performance, a new zero-shot learning method is proposed by grouping attributes and learning discriminant latent features, which can better solve the imbalance problem of constitution categories. Experimental results on our constructed databases validate the proposed methods.
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