Extreme learning machine with autoencoding receptive fields for image classification

2019 ◽  
Vol 32 (12) ◽  
pp. 8157-8173
Author(s):  
Chao Wu ◽  
Yaqian Li ◽  
Zhibiao Zhao ◽  
Bin Liu
Keyword(s):  
Image Classification ◽  
Extreme Learning Machine ◽  
Receptive Fields ◽  
Learning Machine

Extreme learning machine with coefficient weighting and trained local receptive fields for image classification

2020 ◽  
Vol 79 (35-36) ◽  
pp. 26389-26410
Author(s):  
Chao Wu ◽  
Yaqian Li ◽  
Yaru Zhang ◽  
Jing Liu ◽  
Bin Liu
Keyword(s):  
Image Classification ◽  
Extreme Learning Machine ◽  
Receptive Fields ◽  
Learning Machine ◽  
Local Receptive Fields

scholarly journals Label Rectification Learning through Kernel Extreme Learning Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Qiang Cai ◽  
Fenghai Li ◽  
Yifan Chen ◽  
Haisheng Li ◽  
Jian Cao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Extreme Learning Machine ◽  
Classification Performance ◽  
Considerable Progress ◽  
Strong Representation ◽  
Kernel Extreme Learning Machine ◽  
Classification Tasks ◽  
Learning Machine

Along with the strong representation of the convolutional neural network (CNN), image classification tasks have achieved considerable progress. However, majority of works focus on designing complicated and redundant architectures for extracting informative features to improve classification performance. In this study, we concentrate on rectifying the incomplete outputs of CNN. To be concrete, we propose an innovative image classification method based on Label Rectification Learning (LRL) through kernel extreme learning machine (KELM). It mainly consists of two steps: (1) preclassification, extracting incomplete labels through a pretrained CNN, and (2) label rectification, rectifying the generated incomplete labels by the KELM to obtain the rectified labels. Experiments conducted on publicly available datasets demonstrate the effectiveness of our method. Notably, our method is extensible which can be easily integrated with off-the-shelf networks for improving performance.

Image classification based on effective extreme learning machine

2013 ◽  
Vol 102 ◽  
pp. 90-97 ◽  
Author(s):  
Feilong Cao ◽  
Bo Liu ◽  
Dong Sun Park
Keyword(s):  
Image Classification ◽  
Extreme Learning Machine ◽  
Learning Machine
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