抜粋
In training supervised-type neural networks, the quality of the training data is one of the most important factors in deciding the quality of the neural networks. Unfortunately, in real world problems, error-free training data are not always easy to obtain. For complex data, it is always possible that erroneous training samples are included, causing to decrease the performance of the neural networks. In this research, we propose a model of neural network ensemble that, through a competition mechanism, has an ability to automatically train one of its members to learn only from the correct training patterns, thus minimizing the effect of the imperfect data.
| 元の言語 | English |
|---|---|
| ホスト出版物のタイトル | ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age |
| 出版者 | Institute of Electrical and Electronics Engineers Inc. |
| ページ | 2561-2565 |
| ページ数 | 5 |
| 巻 | 5 |
| ISBN（電子版） | 9810475241, 9789810475246 |
| DOI | |
| 出版物ステータス | Published - 2002 |
| イベント | 9th International Conference on Neural Information Processing, ICONIP 2002 - Singapore, Singapore 継続期間: 2002 11 18 → 2002 11 22 |
Other
| Other | 9th International Conference on Neural Information Processing, ICONIP 2002 |
|---|---|
| 国 | Singapore |
| 市 | Singapore |
| 期間 | 02/11/18 → 02/11/22 |
ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
- Signal Processing
フィンガープリント Adaptive neural network ensemble that learns from imperfect supervisor' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。
これを引用
Hartono, P., & Hashimoto, S. (2002). Adaptive neural network ensemble that learns from imperfect supervisor. ： ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age (巻 5, pp. 2561-2565). [1201957] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICONIP.2002.1201957