MATEC Web Conf.
Volume 327, 20202020 4th International Conference on Measurement Instrumentation and Electronics (ICMIE 2020)
|Number of page(s)||4|
|Section||Electronic Materials and Characteristics Analysis|
|Published online||06 November 2020|
A new learning algorithm based on strengthening boundary samples for convolutional neural networks
1 School of Mathematical Sciences, Dalian University of Technology, Dalian, Liaoning, China.
2 Central Laboratory, Dalian Children's Hospital of Dalian medical university, China
3 Institute of Science and Technology for Brain-inspired Intelligence, Fudan University
* Corresponding author: firstname.lastname@example.org
CNN is an artificial neural network that can automatically extract features with relatively few parameters, which is the advantage of CNN in image classification tasks. The purpose of this paper is to propose a new algorithm to improve the classification performance of CNN by strengthening boundary samples. The samples with predicted values near the classification boundary are recorded as hard samples. In this algorithm, the errors of hard samples are added as a penalty term of the original loss function. Multi-classification and binary classification experiments were performed using the MNIST data set and three sub-data sets of CIFAR-10, respectively. The experimental results prove that the accuracy of the new algorithm is improved in both binary classification and multi-classification problems.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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