论文标题
多数投票和condorcet的陪审团定理
Majority Voting and the Condorcet's Jury Theorem
论文作者
论文摘要
There is a striking relationship between a three hundred years old Political Science theorem named "Condorcet's jury theorem" (1785), which states that majorities are more likely to choose correctly when individual votes are often correct and independent, and a modern Machine Learning concept called "Strength of Weak Learnability" (1990), which describes a method for converting a weak learning algorithm into one that achieves arbitrarily high accuracy and stands in the basis of Ensemble Learning.尽管Condorcet定理的直观陈述，但我们找不到经典的机器学习手册中的紧凑而简单的严格数学证明，也没有在发表的论文中。一定要说的是，我们不声称发现或重塑理论或结果。我们谦虚地希望提供对定理的更公开可用的简单推导。我们会发现，看到更多的介绍与手机学习课程的老师使用我们在这里提供的证据作为解释合奏学习动机的一种练习。
There is a striking relationship between a three hundred years old Political Science theorem named "Condorcet's jury theorem" (1785), which states that majorities are more likely to choose correctly when individual votes are often correct and independent, and a modern Machine Learning concept called "Strength of Weak Learnability" (1990), which describes a method for converting a weak learning algorithm into one that achieves arbitrarily high accuracy and stands in the basis of Ensemble Learning. Albeit the intuitive statement of Condorcet's theorem, we could not find a compact and simple rigorous mathematical proof of the theorem neither in classical handbooks of Machine Learning nor in published papers. By all means we do not claim to discover or reinvent a theory nor a result. We humbly want to offer a more publicly available simple derivation of the theorem. We will find joy in seeing more teachers of introduction-to-machine-learning courses use the proof we provide here as an exercise to explain the motivation of ensemble learning.
