AdaBoost的音标

PCA+ SVM, ICA+ SVM, Haar-like+ Adaboost+ SVM and Gabor+ Adaboost+ SVM are implemented to recognize the face gender. The experiment results report that the method proposed in this paper gets the better performance and the accuracy is above 90%.

本文在FERET图像库上作了大量有意义的实验，分别采用PCA+SVM，ICA+SVM，Haar like+Adaboost+SVM和本文提出的Gabor+Adaboost+SVM方法进行人脸性别识别，实验结果表明本文提出的方法性能优越，识别率在90%以上。

To address the problem of multicomponent patterns that many researchers have forgotten to consider it in their classification systems, an adaptive boosting multi-label learning algorithm ( AdaBoost. MC) is developed based on decision trees, maximum a posteriori ( MAP) and the robust ranking principles.

为解决许多研究者在其分类系统中未予考虑的多成份模式问题，开发了一种基于决策树、最大后验（MAP）和鲁棒排序原则的自适应boosting多标记学习算法（AdaBoost.MC）。

AdaBoost algorithm is introduced in detail in this paper and rectangle feature, integral figure and so on are also analyzed. Then how to construct weak classifier, strong classifier and cascade classifier is discussed.

详细地介绍了AdaBoost算法的思想，包括矩形特征、积分图等概念，并对弱分类器、强分类器以及级联分类器的构造过程进行了阐述。