参考文献/References:
[1]PARZEN E. On estimation of a probability density function and mode[J]. The Annals of Mathematical Statistics, 1962, 33(3): 1065-1076.
[2]SCOTT D W. Multivariate density estimation: theory, practice, and visualization[M]. Hoboken, USA: John Wiley & Sons, 2015.
[3]XIANG Zhongliang, YU Xiangru, KANG D K. Experimental analysis of nave Bayes classifier based on an attribute weighting framework with smooth kernel density estimations[J]. Applied Intelligence, 2016, 44(3): 611-620.
[4]何玉林.基于核密度估计的光谱数据分类与回归方法研究[D].保定:河北大学,2014.
HE Yulin. Spectral data classification and regression based on kernel density estimation[D]. Baoding: Hebei University, 2014.(in Chinese)
[5]ANDERSON T K. Kernel density estimation and k-means clustering to profile road accident hotspots[J]. Accident: Analysis & Prevention, 2009, 41(3): 359-364.
[6]张婧虹.混合数据的核密度估计熵与快速的贪心特征选择算法[D] .杭州:浙江大学,2017.
ZHANG Jinghong. Kernel density estimation entropy for hybrid data and a fast greedy feature selection algorithm[D]. Hangzhou: Zhejiang University, 2017.(in Chinese)
[7]NANNI L, LUMINI A. Ensemble of Parzen window classifiers for on-line signature verification[J]. Neurocomputing, 2005, 68(5): 217-224.
[8]WANGD M P, JONES M C. Kernel smoothing[M]. Boca Raton, USA: CRC Press, 1994.
[9]SILVERMAN B W. Density estimation for statistics and data analysis[M]. London: Chapman and Hall, 1986.
[10]TERRELL G R. The maximal smoothing principle in density estimation[J]. Journal of the American Statistical Association, 1990, 85(410): 470-477.
[11]ALEXANDRE L A. A solve-the-equation approach for unidimensional data kernel bandwidth selection[R/OL]. [2008-11-29][2008-01-01]. Beira Interior, Portugal: University of Beira Interior. http:// www.di.ubi.pt/~lfbaa/entnetsPubs/bandwidth.pdf.
[12]茹杨.核函数的核密度估计算法[D].哈尔滨: 哈尔滨理工大学, 2016.
RU Yang. Algorithm of kernel density estimation of kernel function[D]. Harbin: Harbin University of Science and Technology, 2016.(in Chinese)
[13]王俊明,茹杨,陈瑜,等.基于余弦核函数在solve-the-equation方法下的核密度估计[J].哈尔滨理工大学学报,2016,21(1):114-117.
WANG Junming, RU Yang, CHEN Yu, et al. Solve-the-equation kernel density estimation method based on cosine kernel function[J]. Journal of Harbin University of Science and Technology, 2016, 21(1): 114-117.(in Chinese)
[14]HORVITZ D G, THOMPSON D J. A generalization of sampling without replacement from a finite universe[J]. Journal of the American Statistical Association, 1952, 47(260): 663-685.
[15]BREIMAN L. Bagging predictors[J]. Machine Learning, 1996, 24(2): 123-140.
[16]MARTINEZ-MUNOZ G, SUAREZ A. Out-of-bag estimation of the optimal sample size in bagging[J]. Pattern Recognition, 2010, 43(1): 143-152.
[17]LUH K, PIPPENGER N. Large-deviation bounds for sampling without replacement[J]. The American Mathematical Monthly, 2014, 121(5): 449-454.
[18]HE Yulin, LIU J N K, WANG Xizhao, et al. Optimal bandwidth selection for re-substitution entropy estimation[J]. Applied Mathematics and Computation, 2012, 219(8): 3425-3460.