.png){kind=icon}
返回
Ant colony optimization for learning Bayesian networks
.png){kind=icon}
OA
AI
DOI：10.1016/S0888-613X(02)00091-9.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
One important approach to learning Bayesian networks (BNs) from data uses a scoring metric to evaluate the fitness of any given candidate network for the data base, and applies a search procedure to explore the set of candidate networks. The most usual search methods are greedy hill climbing, either deterministic or stochastic, although other techniques have also been used. In this paper we propose a new algorithm for learning BNs based on a recently introduced metaheuristic, which has been successfully applied to solve a variety of combinatorial optimization problems: ant colony optimization (ACO). We describe all the elements necessary to tackle our learning problem using this metaheuristic, and experimentally compare the performance of our ACO-based algorithm with other algorithms used in the literature. The experimental work is carried out using three different domains: ALARM, INSURANCE and BOBLO. (C) 2002-Elsevier Science Inc. All rights reserved.
Keyword:
Bayesian networks
learning
ant colony optimization
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3
论文数:
3.0K
被引数:
5.1K
机构
暂无机构信息
引用论文
.png){kind=icon}
Asymptotics of reaction–diffusion fronts with one static and one diffusing reactant具有一个静态和一个扩散反应物的反应扩散前沿的渐近性OAAI
OAAI分享收藏Studies on flavonoid metabolism. Identification of the metabolities of (+)-catechin in rat urineOAAI
OAAI分享收藏
查看更多内容