A high-level network of neural classifiers
The high-level Neural Network model described in this paper is a multi-layered feedforward network where each hidden and output unit is also a Neural Network. Each of the units which compose the Neural Network, termed classifier unit, is an incremental network that adjusts its architecture depending...
Saved in:
| 主要作者: | |
|---|---|
| 格式: | text |
| 出版: |
Animo Repository
2001
|
| 主題: | |
| 在線閱讀: | https://animorepository.dlsu.edu.ph/faculty_research/11962 |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 機構: | De La Salle University |
| 總結: | The high-level Neural Network model described in this paper is a multi-layered feedforward network where each hidden and output unit is also a Neural Network. Each of the units which compose the Neural Network, termed classifier unit, is an incremental network that adjusts its architecture depending on the complexity of the input-output association task that is assigned to it. The various ways by which such a high-level Neural Network can learn are presented. These are discussed in the context of hybrid systems which incorporate the advantages of Expert Systems and Neural Networks. |
|---|