Improving Convolutional Neural Network (CNN) architecture (miniVGGNet) with batch normalization and learning rate decay factor for image classification

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Improving Convolutional Neural Network (CNN) architecture (miniVGGNet) with batch normalization and learning rate decay factor for image classification

The image classification is a classical problem of image processing, computer vision, and machine learning. This paper presents an analysis of the performance using Convolutional Neural Network (CNN) for image classifying using deep learning. MiniVGGNet is CNN architecture used in this paper to trai...

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Bibliographic Details
Main Authors:	Ismail, Asmida, Ahmad, Siti Anom, Che Soh, Azura, Hassan, Mohd Khair, Harith, Hazreen Haizi
Format:	Article
Language:	English
Published:	UTHM Publisher 2019
Online Access:	http://psasir.upm.edu.my/id/eprint/80197/ http://psasir.upm.edu.my/id/eprint/80197/1/Improving%20Convolutional%20Neural%20Network%20%28CNN%29%20architecture%20%28miniVGGNet%29%20with%20Batch%20Normalization%20and%20Learning%20Rate%20Decay%20Factor%20for%20Image%20Classification.pdf

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