Multi-layer Boosting for Pattern Recognition

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.


Published in:
Pattern Recognition Letter, 30, 237-241
Year:
2009
Laboratories:




 Record created 2010-02-11, last modified 2018-03-17

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