Two neural network construction methods
| Type of publication: | Journal paper |
| Citation: | Thimm-97.5 |
| Journal: | Neural Processing Letters |
| Volume: | 6 |
| Number: | 01 |
| Year: | 1997 |
| Abstract: | Two low complexity methods for neural network construction, that are applicable to various neural network models, are introduced and evaluated for high order perceptrons. The methods are based on a Boolean approximation of real-valued data. This approximation is used to construct an initial neural network topology which is subsequently trained on the original (real-valued) data. The methods are evaluated for their effectiveness in reducing the network size and increasing the network's generalization capabilities in comparison to fully connected high order perceptrons. |
| Userfields: | ipdmembership={learning}, |
| Keywords: | Boolean logic, connectionism, high order neural network, high order perceptron, ontogenic neural network |
| Projects |
Idiap |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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