决策树的最小化很难近似

18th IEEE Annual Conference on Computational Complexity, 2003. Proceedings. Pub Date : 2003-07-07 DOI:10.1109/CCC.2003.1214412

Detlef Sieling

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引用次数: 32

摘要

决策树是离散函数的表示形式，在复杂性理论、数据挖掘和探索等领域有着广泛的应用。在这些领域，获得小尺寸的决策树是很重要的。决策树的最小化问题是np困难的。这个问题甚至很难近似到任何常数因子。

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Minimization of decision trees is hard to approximate

Decision trees are representations of discrete functions with widespread applications in, e.g., complexity theory and data mining and exploration. In these areas it is important to obtain decision trees of small size. The minimization problem for decision trees is known to be NP-hard. The problem is even hard to approximate up to any constant factor.

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18th IEEE Annual Conference on Computational Complexity, 2003. Proceedings.

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