<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper aims to eradicate the poor performance of the convolutional neural network (CNN) for intelligent analysis and detection in samples. Moreover, to avoid overfitting of the CNN model during the training process, an algorithm is proposed for the fusion of maximum pooled and weight decay. Firstly, the maximum pooled method for the pooling layer is explored after mask processing to reduce the number of irrelevant neurons. Secondly, when updating the neuron weight parameters, the weight decay is introduced to further cut down complexity in model training. The experimental comparison shows that the overfitting avoidance algorithm can reduce the detection error rate by more than 10% in image detection than other methods, and it has better generalisation.</p>
</abstract>

This paper aims to eradicate the poor performance of the convolutional neural network (CNN) for intelligent analysis and detection in samples. Moreover, to avoid overfitting of the CNN model during the training process, an algorithm is proposed for the fusion of maximum pooled and weight decay. Firstly, the maximum pooled method for the pooling layer is explored after mask processing to reduce the number of irrelevant neurons. Secondly, when updating the neuron weight parameters, the weight decay is introduced to further cut down complexity in model training. The experimental comparison shows that the overfitting avoidance algorithm can reduce the detection error rate by more than 10% in image detection than other methods, and it has better generalisation.

Algorithm of overfitting avoidance in CNN based on maximum pooled and weight decay

Computer Science, College of Arts & Science

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper aims to eradicate the poor performance of the convolutional neural network (CNN) for intelligent analysis and detection in samples. Moreover, to...

p	0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	0.9
ICNN	6.36	6.36	6.36	6.36	6.36	6.36	6.36	6.36	6.36
INN_FD	7.27	6.85	6.50	6.23	6.20	6.31	6.38	6.40	6.51
SCP	2.23	2.17	2.10	2.12	2.08	2.26	2.31	2.37	2.49
MD_WD	2.21	2.15	2.03	1.95	1.63	1.88	2.19	2.26	2.27

p	0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	0.9
ICNN	7.44	7.44	7.44	7.44	7.44	7.44	7.44	7.44	7.44
INN_FD	8.87	7.95	7.76	7.10	6.15	7.21	7.38	8.12	8.69
SCP	4.83	4.67	4.55	4.31	4.09	3.92	4.16	4.47	5.39
MD_WD	3.21	3.08	2.97	2.93	2.81	2.88	3.08	3.16	3.35

Algorithm of overfitting avoidance in CNN based on maximum pooled and weight decay

Publicado en línea: 22 ago 2022

Páginas: 965 - 974

Recibido: 25 oct 2021

Aceptado: 15 may 2022

DOI: https://doi.org/10.2478/amns.2022.1.00011

Palabras clave
CNN, weight decay, convolution neural network, overfitting

© 2022 Guanzhan Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

The error rate (%) under different maintenance probabilities in CVC-ClincDB.

The error rate (%) under different maintenance probabilities in CIFAR-10.

Algorithm of overfitting avoidance in CNN based on maximum pooled and weight decay

Publicado en línea: 22 ago 2022

Páginas: 965 - 974

Recibido: 25 oct 2021

Aceptado: 15 may 2022

DOI: https://doi.org/10.2478/amns.2022.1.00011

Palabras claveCNN, weight decay, convolution neural network, overfitting

© 2022 Guanzhan Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

The error rate (%) under different maintenance probabilities in CVC-ClincDB.

The error rate (%) under different maintenance probabilities in CIFAR-10.

Palabras clave
CNN, weight decay, convolution neural network, overfitting