A study of sample size with neural network
| Autor: | Yingjin Cui, Chao-Kun Cheng, Xue Bai, Steven Davis |
|---|---|
| Rok vydání: | 2005 |
| Předmět: |
Artificial neural network
Computer science business.industry Generalization Time delay neural network Pattern recognition Sample (statistics) computer.software_genre Probabilistic neural network Sample size determination Multilayer perceptron Artificial intelligence Data mining business computer Linear separability |
| Zdroj: | Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826). |
| DOI: | 10.1109/icmlc.2004.1380382 |
| Popis: | This study investigated sample complexity for a linearly separable dataset by training and testing a breast cancer database. This study considered two networks: a single layer network and a multilayer network. We observed that the training sample size could be 1 for both networks with good generalization results under different conditions. The multilayer network performed well with any training sample but the single layer network required selection of a training sample having informative class output value. When the multilayer network was trained with a small training sample and the threshold for the testing network output was set at an appropriate value the test error became as low as 2%. We concluded that for a linearly separable dataset it is possible achieve good performance by training a network with small sample size, such as 1 or 2. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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