.png){kind=icon}
返回
Bayesian network classification using spline-approximated kernel density estimation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2004.12.008.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The likelihood for patterns of continuous features needed for probabilistic inference in a Bayesian network classifier (BNC) may be computed by kernel density estimation (KDE), letting every pattern influence the shape of the probability density. Although usually leading to accurate estimation, the KDE suffers from computational cost making it unpractical in many real-world applications. We smooth the density using a spline thus requiring for the estimation only very few coefficients rather than the whole training set allowing rapid implementation of the BNC without sacrificing classifier accuracy. Experiments conducted over a several real-world databases reveal acceleration in computational speed, sometimes in several orders of magnitude, in favor of our method making the application of KDE to BNCs practical. (c) 2005 Elsevier B.V. All rights reserved.
Keyword:
Bayesian networks
classification
kernel density estimation
Naive Bayesian classifier spline
期刊
IF:
3.3
论文数:
7.9K
被引数:
1.6W
机构
暂无机构信息
引用论文
.png){kind=icon}
Fourier transform infrared study of amorphous N2O4 solid: Destabilization with inert impurities
PREAI
分享收藏Adaptive tuning of digitally controlled switched mode power supplies based on desired phase margin
PREAI
分享收藏
查看更多内容