Research Journal of Applied Sciences
ISSN: Online 1993-6079ISSN: Print 1815-932x
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Prospects for Multiple Reductions in Test Samples with a Multivariate, Multicriteria, The Neural Network Statistical Analysis of Biometric Data
Gulzhanat Beketova, Kaiyrkhan Mukapil, Zhibek Alibiyeva, Alexander Ivanov, Alexander Malygin, Berik Akhmetov and Nazym Zhumangalieva
Page: 956-967 | Received 21 Sep 2022, Published online: 21 Sep 2022
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Abstract
It is shown that the classical Chi-Square test has insufficient capacity for efficient processing of biometric data. It is shown that there is a possibility to increase the power of statistical processing through the use of several well-known statistical tests, through the neural network combining their private decisions. Contains tables of formulas promising statistical criteria that complement already used statistical tests. Considered the influence of quantization errors caused by the small amount of experience in the test sample. Proposed to raise the reliability of the estimates due to the digital smoothing of histograms with uniform quantization step. Shows the tables and nomograms to assess the reduction in the probability of errors of the first and second order transition to multivariate statistical analysis of biometric data.
How to cite this article:
Gulzhanat Beketova, Kaiyrkhan Mukapil, Zhibek Alibiyeva, Alexander Ivanov, Alexander Malygin, Berik Akhmetov and Nazym Zhumangalieva. Prospects for Multiple Reductions in Test Samples with a Multivariate,
Multicriteria, The Neural Network Statistical Analysis of Biometric Data.
DOI: https://doi.org/10.36478/rjasci.2015.956.967
URL: https://www.makhillpublications.co/view-article/1815-932x/rjasci.2015.956.967