Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses-Reference-Cited by-同舟云学术

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Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses

Published:2017-12 Issue: Volume:267 Page:69-84
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Wang Mingjing,Chen Huiling,Yang Bo,Zhao Xuehua,Hu Lufeng,Cai ZhenNao,Huang Hui,Tong Changfei

Funder

National

Natural Science Foundation of China

Science and Technology Plan Project of Wenzhou of China

Jilin Province Natural Science Foundation

Zhejiang Provincial Natural Science Foundation of China

Guangdong Natural Science Foundation

Ministry of Education

Graduate Innovation Fund of Wenzhou University

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference50 articles.

1. Extreme learning machine: theory and applications;Huang;Neurocomputing,2006

2. Multicategory classification using an extreme learning machine for microarray gene expression cancer diagnosis;Zhang;IEEE/ACM Trans. Comput. Biol. Bioinf.,2007

3. An efficient machine learning approach for diagnosis of paraquat-poisoned patients;Hu;Comput. Biol. Med.,2015

4. Using blood indexes to predict overweight statuses: an extreme learning machine-based approach;Chen;PLoS ONE,2015

5. Human face recognition based on multidimensional PCA and extreme learning machine;Mohammed;Pattern Recognit.,2011

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