Boosting Using Branching Programs
Boosting Using Branching Programs
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使用分支程序进行提升
DOI：
10.1006/jcss.2001.1796
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发表时间：
2000
期刊：
影响因子：
--
通讯作者：
David A. McAllester
中科院分区：
文献类型：
--
作者：
Y. Mansour;David A. McAllester
众所周知，鉴于一个弱学习假设，决策树学习可以证明决策树的训练错误是| t | t |。是由弱学习假设确定的共享允许分支程序比相应的决策树更紧凑。 | t |，| t |是分支程序的大小，从而由弱学习假设确定。比决策树更高效。
It is known that decision tree learning can be viewed as a form of boosting. Given a weak learning hypothesis one can show that the training error of a decision tree declines as |T| where |T| is the size of the decision tree and b is a constant determined by the weak learning hypothesis. Here we consider the case of decision DAGs—decision trees in which a given node can be shared by different branches of the tree, also called branching programs (BP). Node sharing allows a branching program to be exponentially more compact than the corresponding decision tree. We show that under the same weak learning assumption used for decision tree learning there exists a greedy BP-growth algorithm whose training error is guaranteed to decline as 2−b`|T|, where |T| is the size of the branching program and b is a constant determined by the weak learning hypothesis. Therefore, from the perspective of boosting theory, branching programs are exponentially more efficient than decision trees.