Multi-layer boosting for pattern recognition

doi:10.1016/j.patrec.2008.09.012

2009-02-01

François Fleuret *

DOI：10.1016/j.patrec.2008.09.012

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摘要

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We extend the standard boosting procedure to train a two-layer classifier dedicated to handwritten character 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 unsupervised EM-based feature extraction. (C) 2008 Elsevier B.V. All rights reserved.

Keyword:

Boosting

Multi-layer perceptron

Functional gradient descent

Convolutional network