Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/892
Título: | Evolutionary design of neural networks for classification and regression |
Autor(es): | Rocha, Miguel Cortez, Paulo Neves, José |
Palavras-chave: | Supervised machine learning Multilayer perceptrons Evolutionary algorithms Ensembles |
Data: | Mar-2005 |
Editora: | Springer |
Citação: | RIBEIRO, B.; ALBRECHT, R.; DOBNIKAR, D., ed. lit. – “Adaptive and natural computing algorithms : proceedings of ICANGA, Coimbra, 2005.”. Springer: New York, 2005. ISBN 3-211-24934-6. p. 304-307. |
Resumo(s): | The Multilayer Perceptrons (MLPs) are the most popular class of Neural Networks. When applying MLPs, the search for the ideal architecture is a crucial task, since it should should be complex enough to learn the input/output mapping, without overfitting the training data. Under this context, the use of Evolutionary Computation makes a promising global search approach for model selection. On the other hand, ensembles (combinations of models) have been boosting the performance of several Machine Learning (ML) algorithms. In this work, a novel evolutionary technique for MLP design is presented, being also used an ensemble based approach. A set of real world classification and regression tasks was used to test this strategy, comparing it with a heuristic model selection, as well as with other ML algorithms. The results favour the evolutionary MLP ensemble method. |
Tipo: | Artigo em ata de conferência |
Descrição: | Comunicação aprovada à ICANGA March 2005, Coimbra. |
URI: | https://hdl.handle.net/1822/892 |
ISBN: | 3211249346 |
Versão da editora: | The original publication is available at http://www.springerlink.com |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | DI/CCTC - Artigos (papers) DSI - Engenharia da Programação e dos Sistemas Informáticos |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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ednncr.pdf | 79,57 kB | Adobe PDF | Ver/Abrir |