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...
محفوظ في:
| المؤلف الرئيسي: | |
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| التنسيق: | text |
| منشور في: |
Animo Repository
2001
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://animorepository.dlsu.edu.ph/faculty_research/11962 |
| الوسوم: |
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| الملخص: | 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. |
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