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. |
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| Projects |
Idiap IM2 |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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