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We use a boosting procedure on patches represented as gray codes to build a strong classifier useful to distinguish between eye and non-eye patches.
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To build a powerful strong classifier, basis classifiers which have higher precision must take more responsibility in categorization process.
Since Tang et al.'s (9) approach looked promising, we integrated their four specialized classifiers into our machine learning framework in order to build a strong baseline.
But what features of human interaction build a strong tie?
These weak classifiers are used to construct a strong classifier,.
We use a set of weak classifiers to construct a strong classifier.
However, when the precision of weak classifiers is lower, more weak classifiers are needed to construct a strong classifier.
Output: A strong classifier.
They implement a strong classifier by integrating many weak classifiers.
In the AdaBoost framework, a strong classifier consists of weak classifiers connected sequentially.
A strong classifier is a classifier which is supposed to have high detection performance.
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