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AdaBoost algorithm is a kind of very important feature classification machine learning algorithm, But if difficult samples exist in the training samples, With the iterative Number increasing, this easily leads to degeneration Phenomenon, and reduces the generalization ability of the classifier. In view of the face detection under complex background degeneration appeared problem, This article Proposes...
AdaBoost algorithm is a kind of very important feature classification machine learning algorithm, But if difficult samples exist in the training samples, with the iterative Nurnber increasing, this easily leads to degeneration Phenomenon, and reduces the generalization ability of the classifier. In view of the face detection under complex background degeneration appeared problem, This article Proposes...
In this paper, for every local feature, we propose to learn its similar local features across all positive images, instead of using heuristic distance as similarity measure. Specifically, multiple instance learning (MIL) is employed to simultaneously determine the similar points of a local feature and learn its corresponding discriminative function which can be regarded as some kind of similarity...
AdaBoost was proposed as an efficient algorithm of the ensemble learning field, it selects a set of weak classifiers and combines them into a final strong classifier. However, conventional AdaBoost is a sequential forward search procedure using the greedy selection strategy, redundancy can not be avoided. We proposed a post optimization procedure for the found classifiers and their coefficients based...
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