Boosting methods have been introduced in the late 1980's. They were born
following the theoritical aspect of PAC learning. The main idea of boosting
methods is to combine weak learners to obtain a strong learner. The weak
learners are obtained iteratively by an heuristic which tries to correct the
mistakes of the previous weak learner. In 1995, Freund and Schapire [18]
introduced AdaBoost, a boosting algorithm that is still widely used today.
Since then, many views of the algorithm have been proposed to properly tame its
dynamics. In this paper, we will try to cover all the views that one can have
on AdaBoost. We will start with the original view of Freund and Schapire before
covering the different views and unify them with the same formalism. We hope
this paper will help the non-expert reader to better understand the dynamics of
AdaBoost and how the different views are equivalent and related to each other.

通过整合多个弱学习器以构建强学习器，本论文综合了多个视角对 AdaBoost 算法的动态性进行探究，并将它们以相同的形式统一起来，旨在帮助非专业读者更好地理解 AdaBoost 的动态和各种视角之间的等价性和关联性。

AdaBoost 综述：和解视角以更好地理解其动态

Overview of AdaBoost : Reconciling its views to better understand its  dynamics

An important aspect of data integration involves answering queries using
various resources rather than by accessing database relations. The process of
transforming a query from the database relations to the resources is often
referred to as query folding or answering queries using views, where the views
are the resources. We present a uniform approach that includes as special cases
much of the previous work on this subject. Our approach is logic-based using
resolution. We deal with integrity constraints, negation, and recursion also
within this framework.

通过使用视图回答查询，将数据库关系转换为资源的过程通常称为查询折叠或视图查询，我们提出了一种统一的基于逻辑推理的方法，可以处理完整性约束、否定和递归。