Evidence Contrary to the Statistical View of Boosting
Evidence Contrary to the Statistical View of Boosting
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DOI：
10.5555/1390681.1390687
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发表时间：
2008-06
期刊：
影响因子：
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通讯作者：
David Mease;A. Wyner
中科院分区：
文献类型：
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作者：
David Mease;A. Wyner
从统计学的角度来看，boosting算法侧重于优化，与逻辑回归的最大似然估计相似。在本文中，我们提出的经验证据，提出了这个观点的问题。虽然统计的角度提供了一个理论框架内，它是可能的，推导定理，并在一般情况下创建新的算法，我们表明，仍然有许多悬而未决的重要问题。此外，我们提供的例子，揭示了许多实际的建议和新的方法，是从统计的角度来的关键缺陷。我们使用简单的模拟模型进行精心设计的实验，以说明其中的一些缺陷及其实际后果。
The statistical perspective on boosting algorithms focuses on optimization, drawing parallels with maximum likelihood estimation for logistic regression. In this paper we present empirical evidence that raises questions about this view. Although the statistical perspective provides a theoretical framework within which it is possible to derive theorems and create new algorithms in general contexts, we show that there remain many unanswered important questions. Furthermore, we provide examples that reveal crucial flaws in the many practical suggestions and new methods that are derived from the statistical view. We perform carefully designed experiments using simple simulation models to illustrate some of these flaws and their practical consequences.