Abstract
Real AdaBoost algorithm demands division of the sample space. The traditional finite division can not reflect the distribution of positive and negative samples. In this paper,a new real AdaBoost algorithm based on multi-threshold method was developed. Through the selection method of multi-optimization threshold and combining the strategy of weak classifier threshold selection in discrete AdaBoost algorithm,the rational division of sample space was implemented. The experimental results on MIT-CBCL database prove the improved real AdaBoost algorithm increases the detection rate by 0.5% and 2% than the traditional finite division algorithm and real AdaBoost altorithm,and decrease the error rate by 0.15% and 0.27%,and its convergence is faster.
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