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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/43585

Title: A Jacobian Matrix-based Learning Machine and Its Applications in Medical Diagnosis
Authors: Mu-Chun Su;Yi-Zeng Hsieh;Chen-Hsu Wang;Pa-Chun Wang
Contributors: 國立臺灣海洋大學:電機工程學系
Keywords: Neural networks;Learning algorithm;Jacobian matrix;Pattern recognition;Classifier
Date: 2017-03
Issue Date: 2017-10-12T08:38:26Z
Publisher: IEEE Access
Abstract: Abstract: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 (ICU) survival prediction data set were used for demonstrating the performance of the proposed JMLM.
Relation: pp. 1
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/43585
Appears in Collections:[電機工程學系] 期刊論文

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