扩展决策树的可学习性

[Proceedings] Third International Conference on Tools for Artificial Intelligence - TAI 91 Pub Date : 1991-11-10 DOI:10.1109/TAI.1991.167034

Tapio Elomaa

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引用次数: 2

摘要

作者专注于B. Natarajan(1991)学习离散域全函数类的框架。a . Ehrenfeucht和D. Haussler(1989)已经证明决策树的一个子类在L. Valiant(1984)定义的意义上是可学习的。作者将它们的定义推广到m个域，并证明了约束决策树分类器的可学习性延续到扩展模型

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Extending the learnability of decision trees

The author concentrates on B. Natarajan's (1991) framework for learning classes of total functions of discrete domains. A. Ehrenfeucht and D. Haussler (1989) have shown that a subclass of decision trees is learnable in the sense defined by L. Valiant (1984). The author generalizes their definitions to m-ary domains and shows that the learnability of restricted decision tree classifiers carries over to the extended model.<>

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[Proceedings] Third International Conference on Tools for Artificial Intelligence - TAI 91

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