Open AccessDeep Convolutional Neural Network Based Extreme Learning Machine Image Classification
Author(s) -
G. D. Praveenkumar,
R. Nagaraj
Publication year - 2021
Publication title -
international journal of scientific research in science, engineering and technology
Language(s) - English
Resource type - Journals
eISSN - 2395-1990
pISSN - 2394-4099
DOI - 10.32628/ijsrset1218475
Subject(s) - mnist database , convolutional neural network , computer science , artificial intelligence , extreme learning machine , deep learning , classifier (uml) , benchmark (surveying) , pattern recognition (psychology) , machine learning , contextual image classification , artificial neural network , image (mathematics) , geodesy , geography
In this paper, we introduce a new deep convolutional neural network based extreme learning machine model for the classification task in order to improve the network's performance. The proposed model has two stages: first, the input images are fed into a convolutional neural network layer to extract deep-learned attributes, and then the input is classified using an ELM classifier. The proposed model achieves good recognition accuracy while reducing computational time on both the MNIST and CIFAR-10 benchmark datasets.