EXACT LEARNING BOOLEAN FUNCTIONS VIA THE MONOTONE THEORY

被引：102
|
作者：
BSHOUTY, NH
机构：
[1] Department of Computer Science, The University of Calgary, Calgary
来源：
INFORMATION AND COMPUTATION | 1995年 / 123卷 / 01期
关键词：
D O I：
10.1006/inco.1995.1164
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
We study the learnability of boolean functions from membership and equivalence queries. We develop the Monotone Theory that proves (1) Any boolean function is learnable in polynomial time in its minimal disjunctive normal form size, its minimal conjunctive normal form size, and the number of variables n. In particular, (2) Decision trees are learnable. Our algorithms are in the model of exact learning with membership queries and unrestricted equivalence queries. The hypotheses to the equivalence queries and the output hypotheses are depth 3 formulas. (C) 1995 Academic Press. Inc.
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收藏
页码：146 / 153
页数：8
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