Adaptive Exact Learning of Decision Trees from Membership Queries

被引:0
作者
Bshouty, Nader H. [1 ]
Haddad-Zaknoon, Catherine A. [1 ]
机构
[1] Technion, Dept Comp Sci, IL-32000 Haifa, Israel
来源
ALGORITHMIC LEARNING THEORY, VOL 98 | 2019年 / 98卷
关键词
Decision Trees; Membership Queries; Adaptive Learning; Exact Learning; EFFICIENT; ALGORITHM; GRAPH; DNF;
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
In this paper we study the adaptive learnability of decision trees of depth at most d from membership queries. This has many applications in automated scientific discovery such as drugs development and software update problem. Feldman solves the problem in a randomized polynomial time algorithm that asks (O) over tilde (2(2d)) log n queries. Kushilevitz-Mansour solve it in a deterministic polynomial time algorithm that asks 2(18d+o(d)) log n queries. We improve the query complexity of both algorithms. We give a randomized polynomial time algorithm that asks (O) over tilde (2(2d))+ 2(d) log n queries and a deterministic polynomial time algorithm that asks 2(5.83d) + 2(2d+o(d)) log n queries.
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页数:28
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