An application of codes to attribute-efficient learning

被引：0

作者：

Hofmeister, T ^{[1
]}

机构：

[1] Univ Dortmund, D-44221 Dortmund, Germany

来源：

COMPUTATIONAL LEARNING THEORY | 1999年 / 1572卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We design asymptotically optimal query strategies for the class of parity functions which contain at most k essential variables. The number of questions asked is at most twice the number asked by an optimal strategy. The strategy presented is even non-adaptive. For fixed k, the number of questions is optimal up to additive constants. Our results improve upon results by Uehara, Tsuchida and Wegener [6].

引用

页码：101 / 110

页数：10

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