Understanding of the Convolutional Neural Networks with Relative Learning Algorithms

Jieluo Peng

Abstract

With the development of calculating ability, image detection has become one of the most popular research fields recently. Convolutional Neural Network is a kind of depth feed-forward network, which has been successfully applied in image recognition. Its hierarchical structure provides the power of weight-sharing and down-sampling. The Convolutional Neural Network effectively combines the two stages of feature extraction and classification in the traditional pattern recognition, and applies the gradient descent algorithm and the back-propagation algorithm to realize the network training. This article will explore the structure and function of Convolutional Neural Networks, with the introduction of the back-propagation algorithm. Then it will introduce how to apply Convolutional Neural Networks in the application of face recognition. The advantages of applying Convolutional Neural Network to face recognition are analyzed. This article also introduces the application of Convolutional Neural Network in other aspects as well.

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Paper Citation


in Harvard Style

Peng J. (2018). Understanding of the Convolutional Neural Networks with Relative Learning Algorithms.In 3rd International Conference on Electromechanical Control Technology and Transportation - Volume 1: ICECTT, ISBN 978-989-758-312-4, pages 657-661. DOI: 10.5220/0006976406570661


in Bibtex Style

@conference{icectt18,
author={Jieluo Peng},
title={Understanding of the Convolutional Neural Networks with Relative Learning Algorithms},
booktitle={3rd International Conference on Electromechanical Control Technology and Transportation - Volume 1: ICECTT,},
year={2018},
pages={657-661},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006976406570661},
isbn={978-989-758-312-4},
}


in EndNote Style

TY - CONF

JO - 3rd International Conference on Electromechanical Control Technology and Transportation - Volume 1: ICECTT,
TI - Understanding of the Convolutional Neural Networks with Relative Learning Algorithms
SN - 978-989-758-312-4
AU - Peng J.
PY - 2018
SP - 657
EP - 661
DO - 10.5220/0006976406570661