• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Symposium on Intelligence Computation amp; Applications(ISICA'2007); 20070921-23; Wuhan(CN) >Texture Classification of Aerial Image Based on Bayesian Networks with Hidden Nodes
【24h】

Texture Classification of Aerial Image Based on Bayesian Networks with Hidden Nodes

机译：基于带隐藏节点贝叶斯网络的航空影像纹理分类

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Bayesian networks have emerged in recent years as a powerful data mining technique for handling uncertainty in Artificial Intelligence community. However, researchers in the classification area were not interested in Bayesian networks until the simplest kind of Bayesian networks, Na?ve Bayes Classifiers (NBC), came forth. From that time on, their success led to a recent furry of algorithms for learning Bayesian networks from raw data and triggered experts to explore more deeply into Bayesian networks as classifiers. Although many of learners produce good results on some benchmark data sets, there are still several problems: nodes ordering requirement, computational complexity, lack of publicly available learning tools. Therefore, this paper puts up a new method, Bayesian networks with hidden nodes, which adds some hidden nodes between correlated feature variables to Bayesian networks based on the maximal covariance criterion. Experimental results demonstrate that the proposed method is efficient and effective, and outperforms NBC and Bayesian Network Augmented Naive Bayes (BAN).
机译：近年来，贝叶斯网络已经成为一种强大的数据挖掘技术，用于处理人工智能领域的不确定性。但是，直到最简单的贝叶斯网络，朴素贝叶斯分类器（NBC）出现，分类领域的研究人员才对贝叶斯网络感兴趣。从那时起，他们的成功导致最近涌现出了从原始数据中学习贝叶斯网络的算法，并触发了专家作为分类器更深入地探索贝叶斯网络。尽管许多学习者在某些基准数据集上都取得了不错的成绩，但仍然存在一些问题：节点排序要求，计算复杂性，缺乏公开可用的学习工具。因此，本文提出了一种带有隐藏节点的贝叶斯网络的新方法，该方法基于最大协方差准则将相关特征变量之间的一些隐藏节点添加到贝叶斯网络中。实验结果表明，该方法是有效的，并且优于NBC和贝叶斯网络增强朴素贝叶斯（BAN）。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号