A Jacobian Matrix-Based Learning Machine and Its Applications in Medical Diagnosis

IEEE Access

Mu-Chun Su¹  Yi-Zeng Hsieh²  Chen-Hsu Wang³  Pa-Chun Wang⁴ 

[1] Department of Computer Science and Information Engineering, National Central University, Taoyuan City, Taiwan;Department of Electrical Engineering, National Taiwan Ocean University, Keelung, Taiwan;Medical Intensive Care Unit, Cathay General Hospital, Taipei, Taiwan;Quality Management Center, Cathay General Hospital, Taipei, Taiwan;

关键词: Neural networks;    learning algorithm;    Jacobian matrix;    pattern recognition;    Classifier;   

DOI  :  10.1109/ACCESS.2017.2677458

来源: DOAJ

【 摘 要 】

Owing to many appealing properties, neural networks provide a natural basis for solving different kinds of problems. The performance of neural networks greatly depends on whether they can provide appealing solutions to the problems of the parameter learning (i.e., the connecting weights in each layer) and the structure learning (i.e., the network structure). These two kinds of learning can be performed simultaneously or separately. In this paper, we proposed the Jacobian matrix-based learning machine (JMLM) to provide an appealing solution to the aforementioned two kinds of learning. The network structure of a JMLM can be incrementally constructed and a Jacobian-matrix-based learning method is proposed to efficiently estimate the corresponding network parameters. Furthermore, we can provide physically meaningful explanations to help human analyzers to make decisions based on the parameters embedded in a trained JMLM. One 2-D artificial data set, one benchmark medical data set, and an intensive care unit survival prediction data set were used for demonstrating the performance of the proposed JMLM.

【 授权许可】

Unknown