AdaBoost通过对一些弱分类器(weak classifier)的组合来形成一个强分类器(strong classifier), “提升(boost)”弱分类器得到一个分类性能好的强分类器
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LUT weak classifier 查找表型弱分类器
Weak Classifier and Strong Classifier 弱分类器和强分类器
Look-up-table weak classifier training 查表型弱分类器训练
Aimed at the time-consuming problem of Adaboost face detection algorithm in the training classifier process,a detailed analysis of Adaboost algorithm is carried out,the four-point average method is proposed to speed up and look for the best weak classifier.
针对Adaboost人脸检测算法在分类器训练过程中耗时较多的问题,对Adaboost算法进行了详细分析,提出了加快寻找每一轮最佳弱分类器的四点均值法。
参考来源 - 快速局部遮挡人脸检测算法研究·2,447,543篇论文数据，部分数据来源于NoteExpress
The voting algorithms try to form a powerful classifier by combining multiple weak classifiers as a council of base-classifiers.
基于委员会的方法试图通过合并多个弱分类器建立一个有效的委员会来构造一个更加有效的分类器。
Simulation experiments show when the error rate is in an acceptable range, the algorithms using fewer weak classifiers will be able to guarantee the strong classifier to maintain a high correct rate.
对两种阈值和偏置计算方法的仿真实验结果表明，在错分率降可接受的范围内，二者均使用较少的弱分类器便可获得高识别率的强分类器。
The algorithm used weighted templates to structure each weak learning classifier, which overcame the shortcoming of structuring classifier by using a single feature.
在该演化算法中，采取训练正反类样本加权模板的方法来构造各个弱学习分类器，克服了常规的基于单一特征构造弱分类器的不足。
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