A decision-theoretic generalization of on-line learning and an application to boosting
A decision-theoretic generalization of on-line learning and an application to boosting
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DOI：
10.1006/jcss.1997.1504
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发表时间：
1997-08-01
影响因子：
1.1
通讯作者：
Schapire, RE
中科院分区：
文献类型：
--
作者：
Freund, Y;Schapire, RE
在本文的第一部分中，我们考虑的问题，动态分配资源之间的一组选项在最坏情况下的在线框架。我们研究的模型可以被解释为广泛的，抽象的扩展，以及研究在线预测模型的一般决策理论设置。我们表明，乘法的权重更新Littlestone-Warranty规则可以适应这个模型，产生的界限，在某些情况下，稍微弱，但适用于一个更一般的学习问题。我们展示了如何将由此产生的学习算法应用于各种问题，包括赌博，多结果预测，重复游戏，并预测点的R-n。在论文的第二部分中，我们应用乘法权重更新技术推导出一种新的增强算法。这种增强算法不需要任何先验知识的弱学习算法的性能，我们还研究了推广的新的增强算法的学习函数的范围，而不是二进制，是一个任意的有限集或有界段的真实的线的问题。(C)北京：科学出版社.
In the first part of the paper we consider the problem of dynamically apportioning resources among a set of options in a worst-case on-line framework. The model we study can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting. We show that the multiplicative weight-update Littlestone-Warmuth rule can be adapted to this model, yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems. We show how the resulting learning algorithm can be applied to a variety of problems, including gambling, multiple-outcome prediction, repeated games, and prediction of points in R-n. In the second part of the paper we apply the multiplicative weight-update technique to derive a new boosting algorithm. This boosting algorithm does not require any prior knowledge about the performance of the weak learning algorithm, We also study generalizations of the new boosting algorithm to the problem of learning functions whose range, rather than being binary, is an arbitrary finite set or a bounded segment of the real line. (C) 1997 Academic Press.