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In this paper, we focus on real boosting algorithms (e.g., Real AdaBoost, LogitBoost, or Gentle AdaBoost) which means that weak classifiers are real-valued functions.
Given this set, a real boosting algorithm iteratively finds T weak classifiers h t to form a strong classifier sign ( H ( x ) ) = sign ( ∑ t = 1 T h t ( x ) ) where x is a sample to be classified.
In a real boosting algorithm, the predicted label y∈{-1,1} of a sample x can be seen as a discrete random variable and H x) can be interpreted as the probability of y being an object given the example x (also called the posterior probability) using the following sigmoid function [10]: P ( y = 1 | x ) = e H ( x ) / ( e H ( x ) + e - H ( x ) ). (1).
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