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DAN Yuya
Department Matsuyama University Department of Informatics, Faculty of Informatics Position Professor |
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| Language | Japanese |
| Publication Date | 2024/06 |
| Type | |
| Title | Performance Evaluation of Machine Learning Models using Entropy of Images |
| Contribution Type | |
| Journal | ITE Tech. Rep. |
| Journal Type | Japan |
| Publisher | The Institute of Image Information and Television Engineers (ITE), Japan |
| Volume, Issue, Page | 48(16),pp.17-20 |
| Total page number | 4 |
| Author and coauthor | Riko ABE and Yuya DAN |
| Details | In machine learning with artificial neural networks, there are differences in recognition accuracy in learning models of images classified into classes, depending on the features of the dataset and test images. In particular, we compared models composed of images with high entropy with models composed of images with low entropy for the training data. The entropies used were color image entropy, which is mainly the color information of the image, and differential entropy, which is mainly information about the shape of the image. Specifically, this paper describes the results of experiments comparing the dataset CIFAR-10 with various types of entropy to capture features of images with low recognition accuracy using methods such as back propagation. |
| ISSN | 2424-1970 |