Improving transfer learning accuracy by reusing Stacked Denoising Autoencoders

Transfer learning is a process that allows reusing a learning machine trained on a problem to solve a new problem. Transfer learning studies on shallow architectures show low performance as they are generally based on hand-crafted features obtained from experts. It is therefore interesting to study...

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Vydané v:	Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics s. 1380 - 1387
Hlavní autori:	Kandaswamy, Chetak, Silva, Luis M., Alexandre, Luis A., Sousa, Ricardo, Santos, Jorge M., de Sa, Joaquim Marques
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.10.2014
Predmet:	Computer architecture Deep Learning Error analysis Noise reduction Shape Training Transfer Learning Visualization Yttrium
ISSN:	1062-922X
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