Aspects of encoder implementation in the context of neural networks
There are established results to show that a pattern recognition problem can be handled and trained by a neural network with hidden units. There is also a useful theorem which refers to the type of pattern recognition situations that can be recognised by a perceptron. The perceptron is essentially c...
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| Format: | Theses and Dissertations |
| Language: | English |
| Published: |
2009
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| Online Access: | http://hdl.handle.net/10356/19669 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | There are established results to show that a pattern recognition problem can be handled and trained by a neural network with hidden units. There is also a useful theorem which refers to the type of pattern recognition situations that can be recognised by a perceptron. The perceptron is essentially capable of linear classification. For a given encoder problem, the possibility exists of initially implementing a linear classifier. In the event the linear classifier fails to classify according to a defined convergence criteria, then the implementation with a neural network classifier with one or more hidden units can be considered. |
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