Hi,

Firstly, please note that Adaboost is not the same concept as boosting. Boosting it a more general idea of machine learning model.

I don't think Adaboost is to use partial of the training sample. It uses all of the sample to build weak learners and take all the weak learners together to make better classification.

I guess want you really refer to is stochastic boosting, rather than Adaboost. stochastic boosting would have a such a random sub-sampling step to train each base learner. This kind of takes advantage of the randomness to avoid overfitting and accelerate the training process.  For details, you can see Friedman 1999.
Bagging and boosting are ensemble learning methods that becomes popular in recent 15 years. But typically, boosting can be more powerful than bagging. Bagging was origianlly designed to achieve variance reduction. You can view that as a specific bootstrap method. So such persepctive would be closer to what you really worry about.

谢谢，看了回复后很有帮助，最近在做森林扰动的遥感图像分类，主要用的二叉树的方法，但是由于数据量过大，行列想乘后有几百万个样本，所以对这些样本应用Adaboost不怎么现实，需要bagging这类投票的方法，但是由于bagging的精度限制，想找个更好的方法，谢谢你的建议，想问下R里面有没有stochastic boosting的包啊？谢谢了

CRAN上面有的，是Friedman的一个学生做的，gbm