Deep Convolutional Neural Network Based Extreme Learning Machine Image Classification | Zendy

G. D. Praveenkumar | Zendy; R. Nagaraj | Zendy

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Deep 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.

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