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The performance of a classifier is not suitable for object
classification, since it produces a high hit rate, e.g., 0.999,
but also a high error rate, e.g., 0.5. Nevertheless, the hit rate
is much higher than the error rate. To construct an overall good
classifier, several classifiers are arranged in a cascade, i.e.,
a degenerated decision tree. In every stage of the cascade, a
decision is made whether the image contains the object or not.
This computation reduces both rates. Since the hit rate is close
to one, their multiplication results also in a value close to
one, while the multiplication of the smaller error rates
approaches zero. Furthermore, this speeds up the whole
classification process.
An overall effective cascade is learned by a simple iterative
method. For every stage the classification function is
learned, until the required hit rate is reached. The process
continues with the next stage using the correct classified
positive and the currently misclassified negative examples. The
number of CARTs used in each classifier may increase with
additional stages.

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2005-01-27