scholarly journals RFSEN-ELM: SELECTIVE ENSEMBLE OF EXTREME LEARNING MACHINES USING ROTATION FOREST FOR IMAGE CLASSIFICATION

2017 ◽  
Vol 27 (5) ◽  
pp. 499-517 ◽  
Author(s):  
Zhiyu Zhou ◽  
Ji Chen ◽  
Yacheng Song ◽  
Zefei Zhu ◽  
Xiangqi Liu
Keyword(s):  
Image Classification ◽  
Extreme Learning Machines ◽  
Rotation Forest ◽  
Learning Machines ◽  
Selective Ensemble

Deep convolutional representations and kernel extreme learning machines for image classification

2018 ◽  
Vol 78 (20) ◽  
pp. 29271-29290 ◽  
Author(s):  
Xiaobin Zhu ◽  
Zhuangzi Li ◽  
Xiao-Yu Zhang ◽  
Peng Li ◽  
Ziyu Xue ◽  
...  
Keyword(s):  
Image Classification ◽  
Extreme Learning Machines ◽  
Learning Machines

scholarly journals Optimizing Convolutional Neural Network Parameters for Better Image Classification

2020 ◽  
Author(s):  
Manik Dhingra ◽  
Sarthak Rawat ◽  
Jinan Fiaidhi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Classification ◽  
Web Service ◽  
Recognition Task ◽  
Extreme Learning Machines ◽  
Data Set ◽  
Learning Machines ◽  
Fully Connected ◽  
Fully Connected Networks

The work presented here works on getting higher performances for image recognition task using convolutional neural networks on the MNIST handwritten digits data-set. A range of techniques are compared for improvements with respect to time and accuracy, such as using one-shot Extreme Learning Machines (ELM) in place of the iteratively tuned fully-connected networks for classification, using transfer learning for faster convergence of image classification, and improving the size of data-set and making robust models by image augmentation. The final implementation is hosted on cloud as a web-service for better visualization of the prediction results.

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