论文标题
限制adaboost的周期
Limit Cycles of AdaBoost
论文作者
论文摘要
Adaboost机器学习算法的迭代重量更新可以实现为概率单纯性的动态图。当学习低维数据集时，该算法具有骑自行车行为的趋势，这是本文的主题。 Adaboost的循环行为将自己带到了在算法的一般非周期中无效的指导计算方法。从这些计算特性中，我们在Adaboost的循环行为和持续的分数动力学之间提供了具体的对应关系。然后，我们探讨了此通信的结果，以了解该算法如何处于这种周期状态。我们打算为这项工作做的是成为该机器学习算法的循环动力学的新颖且独立的解释。
The iterative weight update for the AdaBoost machine learning algorithm may be realized as a dynamical map on a probability simplex. When learning a low-dimensional data set this algorithm has a tendency towards cycling behavior, which is the topic of this paper. AdaBoost's cycling behavior lends itself to direct computational methods that are ineffective in the general, non-cycling case of the algorithm. From these computational properties we give a concrete correspondence between AdaBoost's cycling behavior and continued fractions dynamics. Then we explore the results of this correspondence to expound on how the algorithm comes to be in this periodic state at all. What we intend for this work is to be a novel and self-contained explanation for the cycling dynamics of this machine learning algorithm.
