[1]金会赏,何玉林,等.一种改进的可适应变宽核密度估计器[J].深圳大学学报理工版,2019,36(No.6(599-724)):709-717.[doi:10.3724/SP.J.1249.2019.06709]
 JIN Huishang,HE Yulin,CHANG Xiuying,et al.An improved kernel density estimator with adaptive varying bandwidth[J].Journal of Shenzhen University Science and Engineering,2019,36(No.6(599-724)):709-717.[doi:10.3724/SP.J.1249.2019.06709]
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一种改进的可适应变宽核密度估计器()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第36卷
期数:
2019年No.6(599-724)
页码:
709-717
栏目:
电子与信息科学
出版日期:
2019-11-20

文章信息/Info

Title:
An improved kernel density estimator with adaptive varying bandwidth
文章编号:
201906017
作者:
金会赏1何玉林2 3常秀颖1王晓兰1蒋捷2 3
1)沧州职业技术学院信息工程系,河北沧州 061001;2)深圳大学计算机与软件学院大数据所,广东深圳 518060;3)深圳大学大数据系统计算技术国家工程实验室,广东深圳 518060
Author(s):
JIN Huishang1 HE Yulin2 3 CHANG Xiuying1 WANG Xiaolan1 and JIANG Jie2 3
1) Department of Information Engineering, Cangzhou Technical College, Cangzhou 061001, Hebei Province, P.R.China 2) Big Data Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, Guangdong Province, P.R.China 3) National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen 518060, Guangdong Province, P.R.China
关键词:
人工智能概率密度核密度估计可适应变宽最优窗口宽度
Keywords:
artificial intelligence probability density kernel density estimation adaptive varying bandwidth optimal bandwidth
分类号:
TP311
DOI:
10.3724/SP.J.1249.2019.06709
文献标志码:
A
摘要:
可适应变宽核密度估计器是一种基于单个数据点的概率密度估计方法,它以单个数据点为处理对象,利用置信区间交叉法则确定核密度估计器的最优窗口宽度.为加快可适应变宽核密度估计器对最优窗口宽度的寻找,通过引入一种可变的标准差项因子去确定置信区间的上下边界,提出一种改进的可适应变宽核密度估计器.可变标准差项因子的引入不仅加快了可适应变宽核密度估计器搜索最优窗口宽度的速度,且在一定程度上降低了“过平滑”概率密度估计现象发生的风险.仿真结果证实了改进的可适应变宽核密度估计器的可行性和有效性.相比经典的可适应变宽核密度估计器,改进的可适应变宽核密度估计器不仅获得了更快的训练速度(最高降低64%),同时提升了概率密度的估计精度(最高降低63%).
Abstract:
The kernel density estimator with adaptive varying bandwidth (KDE-AVB) estimates the probability density for the given data in a point-wise manner, which determines the optimal bandwidth based on the intersection of confidence intervals (ICI) rule. In order to speed up the search of optimal bandwidth, this paper proposes an improved kernel density estimator with adaptive varying bandwidth (IKDE-AVB) which introduces a flexible standard derivation term factor to calculate the upper and lower boundaries of confidence intervals. The flexible standard derivation term factor not only increases the speed of searching the optimal bandwidth, but also reduces the risk of over-smoothed probability density estimation. The experimental results demonstrate the feasibility and effectiveness of IKDE-AVB. In comparison with the classical KDE-AVB, IKDE-AVB not only obtains the faster training speed but also improves the probability density estimation accuracy.

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备注/Memo

备注/Memo:
Received:2019-03-01;Accepted:2019-05-08 Foundation:National Key R&D Program of China (2017YFC0822604-2); China Postdoctoral Science Foundation (2016T90799); Scientific Research Foundation of Shenzhen University for Newly-introduced Teachers (2018060) Corresponding author:Research associate He Yulin.E-mail: yulinhe@szu.edu.cn Citation:JIN Huishang, HE Yulin, CHANG Xiuying, et al. An improved kernel density estimator with adaptive varying bandwidth[J]. Journal of Shenzhen University Science and Engineering, 2019, 36(6): 709-717.(in Chinese)
更新日期/Last Update: 2019-11-30