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| Formato: | Artículo científico |
| Lenguaje: | en |
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Universidade Anhembi Morumbi
2016
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| Acceso en línea: | https://www.redalyc.org/articulo.oa?id=758079701009 |
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- Multidimensional wavelet neural networks based on polynomial powers of sigmoid: a framework to image verification João Fernando Marar Aron Bordin Arte Wavelets Image processing Pattern recognition Human face verification Artificial neural network Wavelet functions have been used as the activation function in feed forward neural networks. An abundance of R&D has been produced on wavelet neural network area. Some successful algorithms and applications in wavelet neural network have been developed and reported in the literature. However, most of the aforementioned reports impose many restrictions in the classical back propagation algorithm, such as low dimensionality, tensor product of wavelets, parameters initialization, and, in general, the output is one dimensional, etc. In order to remove some of these restrictions, a family of polynomial wavelets generated from powers of sigmoid functions is presented. We described how a multidimensional wavelet neural networks based on these functions can be constructed, trained and applied in pattern recognition tasks. As examples of applications for the method proposed a framework for face verification is presented. 2016 artículo científico 2526-1789 https://www.redalyc.org/articulo.oa?id=758079701009 en http://www.redalyc.org/revista.oa?id=7580 DATJournal application/pdf Universidade Anhembi Morumbi DATJournal (Brasil) Num.2 Vol.1