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Books on the topic 'Automatic classification Statistical methods'

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Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

Statistical methods for speech recognition. Cambridge, Mass: MIT Press, 1997.

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2

Logunova, Oksana, Petr Romanov, and Elena Il'ina. Processing of experimental data on a computer. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1064882.

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Abstract:

The textbook provides information about the main methods and tools for automating computational processes used in data processing; methods for representing and generating models of experimental data; data models and classification of processing tasks; and the organization of the user interface in automated systems for processing experimental data. Contains structured chapters on the specifics of experimental research. The features of using software for processing experimental data are clearly and logically described. Theoretical material and basic algorithms for processing experimental data used in industrial statistics are presented. Examples of processing experimental data in the field of metallurgy and management in higher education are given. Meets the requirements of the Federal state educational standards of higher education of the latest generation. For students and postgraduates of higher educational institutions.

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3

Ambergen, A. W. Statistical uncertainties in posterior probabilities. Amsterdam: Centrum voor Wiskunde en Informatica, 1993.

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4

Vasilʹev, D. V. Uskorennoe statisticheskoe modelirovanie sistem upravlenii͡a︡. Leningrad: Ėnergoatomizdat, Leningradskoe otd-nie, 1987.

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5

T, Denison David G., ed. Bayesian methods for nonlinear classification and regression. Chichester, England: Wiley, 2002.

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6

A, Oliver M., and Webster R, eds. Statistical methods in soil and land resource survey. Oxford [England]: Oxford University Press, 1990.

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7

Canada, Canada Agriculture, ed. Optimal set covering for biological classification. Ottawa: Agriculture Canada, 1993.

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8

Clarke, Stevens H. Probationer recidivism in North Carolina: Measurement and classification of risk. [Chapel Hill]: Institute of Government, University of North Carolina at Chapel Hill, 1988.

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9

Griffiths, A. Evaluation of clustering methods for automatic document classification: Final report for the period October 1982 to September 1984. Sheffield: Dept. of Information Studies, University of Sheffield, 1985.

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10

Seo, Eun-Gyoung. An experiment in automatic indexing with Korean texts: A comparison of syntactico-statistical and manual methods. Ann Arbor, Mich: University Microfilms International, 1993.

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11

Vennik, J. A statistical study of the composition of nearby groups of galaxies. Tallinn: Valgus, 1990.

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12

Shi jie jing ji tong ji yan jiu xin dong xiang ji dui zhongguo de qi shi. Beijing Shi: She hui ke xue wen xian chu ban she, 2013.

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13

Wong, Raymond Sin-Kwok. Association models. Thousand Oaks: Sage, 2010.

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14

Hivatal, Hungary Központi Statisztikai. Munkaügyi statisztikai fogalmak: Módszertani segédlet. Budapest: KSH, 1992.

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15

Wong, Raymond Sin-Kwok. Association models. Thousand Oaks: Sage, 2010.

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16

Association models. Thousand Oaks: Sage, 2010.

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17

Tibshirani, Robert. Bias, variance and prediction error for classification rules. [Toronto]: University of Toronto, Dept. of Statistics, 1996.

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18

Foulkes, Andrea S. Applied statistical genetics with R: For population-based association studies. New York: Springer Verlag, 2009.

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19

Applied statistical genetics with R: For population-based association studies. New York: Springer Verlag, 2009.

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20

Foulkes, Andrea S. Applied statistical genetics with R: For population-based association studies. New York: Springer Verlag, 2009.

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21

1928-, Kruskal Joseph B., and Black Paul, eds. An Indoeuropean classification: A lexicostatistical experiment. Philadelphia: American Philosophical Society, 1992.

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22

Giusti, Antonio. Classification and Data Mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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23

1936-, Rohlf F. James, and Bookstein Fred L. 1947-, eds. Proceedings of the Michigan Morphometrics Workshop. Ann Arbor, Mich: University of Michigan Museum of Zoology, 1990.

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24

Textual information access: Statistical models. London: ISTE, 2011.

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25

The statistical evaluation of medical tests for classification and prediction. Oxford: Oxford University Press, 2003.

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26

Różdżyński, Kazimierz. Mikrozmienność charkterystyk urządzeń termo- i higrometrycznych w automatycznych stacjach meteorologicznych PSHM. Warszawa: Instytut Meteorologii i Gospodarki Wodnej, Państwowy Instytut Badawczy, 2013.

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27

Orlóci, László. Conapack: Program for canonical analysis of classification tables. The Hague, Netherlands: SPB Academic, 1992.

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28

Różdżyński, Kazimierz. Micro-variability of characteristics of thermo- and hygrometric circuits in automatic meteorological stations of the State Hydrological Meteorological Service (PSHM). Warsaw: Institute of Meteorology and Water Management, National Research Institute, 2013.

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29

Leacock, Claudia. Automatic assessment of vocabulary usage without negative evidence. Princeton, N.J: Educational Testing Service, 2001.

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30

Biao zhun, mo xing yu ying yong. Beijing: Ke xue chu ban she, 2011.

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31

1947-, Bookstein Fred L., ed. Morphometrics in evolutionary biology: The geometry of size and shape change, with examples from fishes. [Philadelphia, Pa.]: Academy of Natural Sciences of Philadelphia, 1985.

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32

Sheikh, Kazim. The National Hospital Peer Grouping Project. Baltimore, Md: U.S. Dept. of Health and Human Services, Health Care Financing Administration, 1999.

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33

Grebner, Leah A. Medical coding: Understanding ICD-10-CM and ICD-10-PCS. New York, NY: McGraw-Hill, 2013.

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34

Barnabani, Marco. Costruzione delle serie storiche dell'import, dell'export e dei valori unitari dell'Italia dal 1963 al 1980 secondo la classificazione NACE-CLIO. Firenze: Banca toscana, 1986.

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35

Buck, Carol J. Transitioning to ICD-10-CM coding. St. Louis, Mo: Elsevier, 2012.

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36

Online instruments, data collection, and electronic measurements: Organizational advancements. Hershey, PA: IGI Global, 2013.

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37

Association, American Medical, ed. 2014 ICD-10-PCS draft. St. Louis, Missouri: Elsevier, 2014.

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38

ICD-10-PCS draft 2013. St. Louis, Mo: Elsevier/Saunders, 2014.

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39

2013 ICD-10-CM draft. 2nd ed. St. Louis, Mo: Elsevier, 2013.

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40

J, Buck Carol, ed. 2014 ICD-10-CM draft. 2nd ed. St. Louis, Missouri: Elsevier/Saunders, 2014.

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41

ICD-10 implementation toolkit: For physician practices. Englewood, CO: Medical Group Management Association, 2012.

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42

Measurement data modeling and parameter estimation. Boca Raton, FL: Taylor & Francis, 2011.

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43

Optum learning: Detailed instruction for appropriate ICD-10-PCS coding, 2015. West Valley City, UT: Optum, 2014.

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44

Choi, S. C. Statistical Methods of Discrimination and Classification. Elsevier, 1986.

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45

Statistical Methods of Discrimination and Classification. Elsevier, 1986. http://dx.doi.org/10.1016/c2013-0-06111-7.

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46

Machine learning, neural and statistical classification. New York: Ellis Horwood, 1994.

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47

Machine learning, neural and statistical classification. Englewood Cliffs, N.J: Prentice Hall, 1994.

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48

1969-, Srivastava Ashok, and Sahami Mehran, eds. Text mining: Classification, clustering, and applications. Boca Raton, FL: CRC Press, 2009.

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49

Sahami, Mehran, and Ashok N. Srivastava. Text Mining: Classification, Clustering, and Applications. Taylor & Francis Group, 2009.

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50

Sahami, Mehran, and Ashok N. Srivastava. Text Mining: Classification, Clustering, and Applications. Taylor & Francis Group, 2009.

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