Fine-tuning artificial neural networks automatically
To get the most out of powerful tools expert knowledge is often required. Experts are the ones with the suitable knowledge to tune the tools parameters. In this paper we assess several techniques which can automatically fine tune ANN parameters. Those techniques include the use of GA and Stratified...
| Autor principal: | |
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| Outros Autores: | , , |
| Formato: | book |
| Idioma: | eng |
| Publicado em: |
2007
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| Assuntos: | |
| Texto completo: | https://hdl.handle.net/10216/67390 |
| País: | Portugal |
| Oai: | oai:repositorio-aberto.up.pt:10216/67390 |
| Resumo: | To get the most out of powerful tools expert knowledge is often required. Experts are the ones with the suitable knowledge to tune the tools parameters. In this paper we assess several techniques which can automatically fine tune ANN parameters. Those techniques include the use of GA and Stratified Sampling. The tuning includes the choice of the best ANN structure and the best network biases and their weights. Empirical results achieved in experiments performed using nine heterogeneous data sets show that the use of the proposed Stratified Sampling technique is advantageous. |
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