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 [BibTeX] [Marc21]
Multi-layer Boosting for Pattern Recognition
Type of publication: Journal paper
Citation: Fleuret_PRL_2008
Journal: Pattern Recognition Letter
Volume: 30
Year: 2009
Crossref: Fleuret_Idiap-RR-76-2008:
Abstract: We extend the standard boosting procedure to train a two-layer classifier dedicated to handwritten char- acter recognition. The scheme we propose relies on a hidden layer which extracts feature vectors on a fixed number of points of interest, and an output layer which combines those feature vectors and the point of interest locations into a final classification decision. Our main contribution is to show that the classical AdaBoost procedure can be extended to train such a multi-layered structure by propagating the error through the output layer. Such an extension allows for the selection of optimal weak learners by minimizing a weighted error, in both the output layer and the hidden layer. We provide experimental results on the MNIST database and compare to a classical unsu- pervised EM-based feature extraction.
Projects Idiap
Authors Fleuret, Francois
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