A comparative analysis of a neural network with calculated weights and a neural network with random generation of weights based on the training dataset size
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909628215853056 |
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| author | Geidarov, Polad |
| author_facet | Geidarov, Polad |
| contents | The paper discusses the capabilities of multilayer perceptron neural networks implementing metric recognition methods, for which the values of the weights are calculated analytically by formulas. Comparative experiments in training a neural network with pre-calculated weights and with random initialization of weights on different sizes of the MNIST training dataset are carried out. The results of the experiments show that a multilayer perceptron with pre-calculated weights can be trained much faster and is much more robust to the reduction of the training dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_23876 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A comparative analysis of a neural network with calculated weights and a neural network with random generation of weights based on the training dataset size Geidarov, Polad Machine Learning Artificial Intelligence The paper discusses the capabilities of multilayer perceptron neural networks implementing metric recognition methods, for which the values of the weights are calculated analytically by formulas. Comparative experiments in training a neural network with pre-calculated weights and with random initialization of weights on different sizes of the MNIST training dataset are carried out. The results of the experiments show that a multilayer perceptron with pre-calculated weights can be trained much faster and is much more robust to the reduction of the training dataset. |
| title | A comparative analysis of a neural network with calculated weights and a neural network with random generation of weights based on the training dataset size |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2505.23876 |