Entropy Estimation of the Position of the Barrier Dimension : Applicability Nearsighted and Farsighted Iterative Algorithms for Processing High-Dimensional Data :Applicability Nearsighted and Farsighted Iterative Algorithms for Processing High-Dimensional Data

Berik Akhmetov; Alexander Ivanov; Anis Gilmutdinov; Ivan Ognev; Kaiyrkhan Mukapil

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자료유형: 학술대회자료

저자정보: Berik Akhmetov (Hoja Ahmet Yasaui International Kazakh-Turkish University) Alexander Ivanov (Penza Research Electrotechnical Institute) Anis Gilmutdinov (A.N.Tupolev Kazan National Research Technical University) Ivan Ognev (Moscow Power Engineering Institute (National Research University)) Kaiyrkhan Mukapil (K.I.Satpayev Kazakh National Technical University (KazNTU))

저널정보: 제어로봇시스템학회 제어로봇시스템학회 국제학술대회 논문집 ICCAS 2015

발행연도: 2015.10

수록면: 1,329 - 1,332 (4page)

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It is shown that large neural networks allow solving tasks that cannot classical quadratic forms in linear algebra. Thus the assessment of output entropy of neural network converters biometrics code is possible. The assessment of high-dimensional entropy is based on the symmetrization of the problem of the correlation of biometric data. Entropy of low dimension and high-dimensional entropy are differently connected with equally correlated data. For low-dimensional transformations only short-sighted algorithms, which not capable to bypass local extrema of quality are effective. The algorithms constructed on the accounting of multidimensional entropy are far-sighted, they don"t see local extrema.

#neural networks #address of neural networks #multidimensional entropy

Abstract
1. INTRODUCTION
2. ON THE EQUIVALENCE OF MULTIDIMENSIONAL QUADRATIC
3. SYMMETRIZATION OF CORRELATION COMMUNICATIONS
4. EQUIVALENCE OF ENTROPY OF CODES BEFORE AND FULL SYMMETRIZATION OF CORRELATION COMMUNICATIONS
5. HETEROGENEITY OF OMMUNICATION OF HIGH-DIMENSIONAL ENTROPY WITH EQUALLY CORRELATED DATA
6. CONCLUSION
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