中文说明：Adaboost是一种迭代算法，其核心思想是针对同一个训练集训练不同的分类器(弱分类器)，然后把这 些弱分类器集合起来，构成一个更强的最终分类器(强分类器)。其算法本身是通过改变数据分布来实现的，它根据每次训练集之中每个样本的分类是否正确，以及上次的总体分类的准确率，来确定每个样本的权值。将修改过权值的新数据集送给下层 请点击左侧文件开始预览 ！预览只提供20%的代码片段，完整代码需下载后查看 加载中 侵权举报
English Description：
Adaboost is an iterative algorithm, the core idea is the same training set for training different classifiers (weak classifiers), then thisThese weak classifiers together to form a stronger final classifier (strong classifier). The algorithm itself is achieved by changing the data distribution, and it is correct according to the classification of each training set into each sample, and the accuracy of the overall classification of the last to determine the weight of each sample. The modified value of the new data set to the lower right of the classifier is trained, the final will each get trained classifier fusion finally up, as the final decision-making classifier. Use adaboost classifier may exclude some unnecessary features of the training data, and put the key in the training data above.