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Boosting 和 Bagging 是两种用来提高学习算法准确度的方法,这两种方法通过构造一个预测函数系列,然后以一定的方式将它们组合成一个预测函数.文章将介绍这两种方法以及对他们进行的一些理论分析和实验,并对它们的应用以及将来可能的研究进行讨论.
Boosting and Bagging are two ways to improve the accuracy of your learning algorithms by constructing a series of predictive functions and then combining them in a certain way into a predictive function.This article describes both of these methods, Some theoretical analysis and experiments carried out, and their application and future possible research to be discussed.