Nonparametric Algorithms for Estimating the States of Natural Objects

被引：2
作者：
Lapko A.V. [1 ,2 ]
Lapko V.A. [1 ,2 ]
机构：
[1] Institute of Computational Modeling, Siberian Branch, Russian Academy of Sciences, Academgorodok-50, 44, Krasnoyarsk
[2] Reshetnev Siberian State University of Science and Technology, pr. Krasnoyarskii rabochii 31, Krasnoyarsk
来源：
Lapko, A.V. (lapko@icm.krasn.ru) | 2018年 / Pleiades journals卷 / 54期
基金：
俄罗斯基础研究基金会;
关键词：
choice of the bandwidth; decision rule with advantage gradations; kernel estimation of the probability density; pattern recognition; remote sensing; state of forest tracts;
D O I：
10.3103/S8756699018050047
中图分类号：
学科分类号：
摘要：
Modifications of a nonparametric pattern recognition algorithm corresponding to the maximum likelihood criterion with additional decision functions are considered. The synthesis of the proposed algorithms is based on the analysis of the ratios of the estimates of the probability density distributions of random variables in classes and their functionals with input thresholds. The choice of the thresholds is determined by specific features of the classification problem. The results obtained are applied for assessing the states of forest tracts on the basis of remote sensing data. © 2018, Allerton Press, Inc.
引用
收藏
页码：451 / 456
页数：5
相关论文
共 18 条
  • [1] Borzov S.M., Potaturkin O.I., Efficiency of the Spectral-Spatial Classification of Hyperspectral Imaging Data, Avtometriya, 53, 1, pp. 32-42, (2017)
  • [2] Borzov S.M., Potaturkin O.I., Classification of Hyperspectral Images with Different Methods of Training Set Formation, Avtometriya, 54, 1, pp. 89-97, (2018)
  • [3] Zen'kov I.V., Im S.T., Lapko A.V., Et al., Development and Application of Information Technologies for Studying Natural Resources of Siberian Territories on the Basis of Remote Sensing Data, (2017)
  • [4] Kharuk V.I., Im S.T., Petrov I.A., Et al., Fir Decline and Mortality in the Southern Siberian Mountains, Reg. Environ. Change, 17, 3, pp. 803-812, (2017)
  • [5] Epanechnikov V.A., Nonparametric Estimate of the Multidimensional Probability Density, Teor. Veroyatn. Primen., 14, 1, pp. 156-161, (1969)
  • [6] Parzen E., On Estimation of a Probability Density Function and Mode, Ann. Math. Statist., 33, 3, pp. 1065-1076, (1962)
  • [7] Lapko A.V., Lapko V.A., Nonparametric Estimate of the Parzen-Type Probability Density with an Implicitly Defined Form of the Kernel Function, Izmer. Tekhnika, 6, pp. 14-17, (2016)
  • [8] Lapko A.V., Lapko V.A., Nonparametric Estimate of a Parzen-Type Probability Density with an Implicitly Specified Form of the Kernel, Measur. Techn., 59, 6, pp. 571-576, (2016)
  • [9] Lapko A.V., Lapko V.A., Regression Estimation of the Multidimensional Probability Density and its Properties, Avtometriya, 50, 2, pp. 50-56, (2014)
  • [10] Duong T., Ks: Kernel Density Estimation and Kernel Discriminant Analysis in R, J. Statist. Software, 21, 7, pp. 1-16, (2017)
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