中国科学院软件研究所机构知识库
Advanced  
ISCAS OpenIR  > 软件所图书馆  > 期刊论文
Title:
改进的布尔公式学习算法
Alternative Title: Improved Learning Algorithm of Boolean Formula
Author: 哈晓琳 ; 李勇坚
Keyword: 布尔公式 ; 学习算法 ; 询问模型 ; 单调理论 ; 最小赋值向量 ; boolean formula ; learning algorithm ; query model ; monotone theory ; minterms
Source: 计算机系统应用
Issued Date: 2014
Issue: 9, Pages:83-88
Department: 中国科学院软件研究所计算机科学国家重点实验室,北京 100190; 中国科学院大学,北京 100190 中国科学院软件研究所计算机科学国家重点实验室,北京,100190
Abstract: 当前,布尔公式学习算法的研究大多数是理论上的模型建立和推导,很少有人考虑到布尔公式学习算法在实际应用中的效率改进。现在较成熟的布尔学习算法主要利用的是询问模型,而询问模型需要依赖外部的SMT 工具进行询问问题的回答。虽然,布尔公式学习算法可以在多项式次数的询问之后得到正确结果,但是,减少询问的次数可以减少使用 SMT 工具进行问题计算的次数,即减少问题计算的时间。主要针对布尔公式学习算法在实际系统中的应用问题,提出了利用单调理论中的最小赋值向量的方法,来减少布尔公式学习算法的询问次数,提高算法效率和适用性。 Currently, most of the study of Boolean formula learning is theoretically the modeling and derivation. Less people considers efficiency improvements in practical applications. Now more mature Boolean formula learning algorithms mainly use the query model, and the query model relies on external SMT tools to answer the query problems. Although Boolean formula learning algorithm can get the right result after a polynomial number of queries, reducing the number of queries can reduce the number of using SMT tools to answer the queries, namely to reduce the computing time of the queries. This paper took the smallest monotonous assignment vector of the monotone theory to reduce the number of queries in Boolean formula learning algorithm and improved the efficiency of the learning algorithm.
Language: 中文
Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/16970
Appears in Collections:软件所图书馆_期刊论文

Files in This Item:

There are no files associated with this item.


Recommended Citation:
哈晓琳,李勇坚. 改进的布尔公式学习算法[J]. 计算机系统应用,2014-01-01(9):83-88.
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[哈晓琳]'s Articles
[李勇坚]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[哈晓琳]‘s Articles
[李勇坚]‘s Articles
Related Copyright Policies
Null
Social Bookmarking
Add to CiteULike Add to Connotea Add to Del.icio.us Add to Digg Add to Reddit
所有评论 (0)
暂无评论
 
评注功能仅针对注册用户开放,请您登录
您对该条目有什么异议,请填写以下表单,管理员会尽快联系您。
内 容:
Email:  *
单位:
验证码:   刷新
您在IR的使用过程中有什么好的想法或者建议可以反馈给我们。
标 题:
 *
内 容:
Email:  *
验证码:   刷新

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.

 

 

Valid XHTML 1.0!
Copyright © 2007-2019  中国科学院软件研究所 - Feedback
Powered by CSpace