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Books on the topic 'Classification probabiliste'

Author: Grafiati

Published: 12 October 2024

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1

Hack, Henri Robert George Kenneth. Slope stability probability classification: SSPC = Helling stabiliteit classificatie : SSPC. Delft: International Institute for Aerospace Survey and Earth Sciences, 1996.

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2

Spray, Judith A. Multiple-category classification using a sequential probability ratio test. Iowa City, Iowa: American College Testing Program, 1993.

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3

Spray, Judith A. Multiple-category classification using a sequential probability ratio test. Iowa City, Iowa: American College Testing Program, 1993.

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4

Spray, Judith A. Multiple-category classification using a sequential probability ratio test. Iowa City, Iowa: American College Testing Program, 1993.

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5

1967-, Meira Wagner, ed. Demand-driven associative classification. London: Springer, 2011.

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6

Lin, Chuan-Ju. Effects of item-selection criteria on classification testing with the sequential probability ratio test. Iowa City, Iowa: ACT, Inc., 2000.

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7

Lin, Chuan-Ju. Effects of item-selection criteria on classification testing with the sequential probability ratio test. Iowa City, Iowa: ACT, Inc., 2000.

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8

Lin, Chuan-Ju. Effects of item-selection criteria on classification testing with the sequential probability ratio test. Iowa City, Iowa: ACT, Inc., 2000.

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9

Baram, Yoram. Estimation and classification by sigmoids based on mutual information. [Washington, D.C: National Aeronautics and Space Administration, 1994.

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10

Classification Group of SIS. Meeting. Classification and data analysis: Theory and application : proceedings of the biannual meeting of the Classification Group of Societa Italia di Statistica (SIS), Pescara, July 3-4, 1997. Edited by Vichi Maurizio 1959- and Opitz Otto. New York: Springer, 1999.

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11

Marc, Aerts, ed. Topics in modelling of clustered data. Boca Raton: Chapman & Hall/CRC, 2002.

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12

International Federation of Classification Societies. Conference. Advances in data science and classification: Proceedings of the 6th Conference of the International Federation of Classification Societies (IFCS-98), Università "La Sapienza", Rome, 21-24 July, 1998. New York: Springer, 1998.

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13

Agency, International Atomic Energy, ed. Collection and classification of human reliability data for use in probabilistic safety assessments: Final report of a co-ordinated research programme 1995-1998. Vienna: International Atomic Energy Agency, 1998.

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14

Kaufman, Leonard. Finding groups in data: An introduction to cluster analysis. Hoboken, N.J: Wiley, 2005.

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15

Kaufman, Leonard. Finding groups in data: An introduction to cluster analysis. New York: Wiley, 1990.

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16

J, Hayes Richard, and Hayes Richard J. Cluster randomised trials. Boca Raton: Taylor & Francis, 2009.

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17

Leitgeb, Hannes. Probability in Logic. Edited by Alan Hájek and Christopher Hitchcock. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199607617.013.35.

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This chapter is about probabilistic logics: that is, systems of logic in which logical consequence is defined in probabilistic terms. The chapter starts with some historical background, covering the longstanding association of the study of logic with probability. There follows a clarification of the terms to be used. Then these systems of probabilistic logic are classified and some key references given. Two chief classifications are presented. Then one class of probabilistic logics, those that derive from Ernest Adams’ work, is presented in more detail. In reviewing Adams’ work, six types of semantics are presented. These are examined via six case studies.

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18

Classification. 2nd ed. Boca Raton: Chapman & Hall/CRC, 1999.

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19

Marriott, F. H. C., W. J. Krzanowski, and F. H. Marriott. Multivariate Analysis: Classification, Covariance Structures & Repeated Measurements. A Hodder Arnold Publication, 1998.

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20

Comparisons of neural networks to standard techniques for image classification and correlation. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1994.

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21

Mixture Model-Based Classification. Taylor & Francis Group, 2016.

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22

McNicholas, Paul D. Mixture Model-Based Classification. Taylor & Francis Group, 2016.

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23

McNicholas, Paul D. Mixture Model-Based Classification. Taylor & Francis Group, 2020.

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24

McNicholas, Paul D. Mixture Model-Based Classification. Taylor & Francis Group, 2016.

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25

McNicholas, Paul D. Mixture Model-Based Classification. Taylor & Francis Group, 2016.

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26

McNicholas, Paul D. Mixture Model-Based Classification. Taylor & Francis Group, 2016.

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27

Tjärnlund, Anna, and Ingrid E. Lundberg. Diagnostic and classification criteria. Edited by Hector Chinoy and Robert Cooper. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198754121.003.0002.

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Diagnosis of idiopathic inflammatory myopathies (IIM) is based on clinical features such as subacute progress of symmetrical weakness of proximal muscle and muscle fatigue, in combination with laboratory confirmation of myopathy, including elevated muscle enzyme levels in serum and histological demonstration of skeletal muscle inflammation, as well as fibre regeneration and degeneration in muscle biopsies. Several classification criteria for IIM have historically been proposed. New classification criteria for IIM have been developed, and are based on real patient data from adult and juvenile IIM cases worldwide. These criteria provide a probability of having IIM with defined cut-off values for categorizing ‘possible’, ‘probable’, and ‘definite’ IIM. Autoantibodies in IIM are becoming increasingly important for diagnosis and classification, and newly identified autoantibodies specific for inclusion body myositis may provide a future diagnostic tool.

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28

Probability theory subject indexes from Mathematical reviews. Providence, R.I: American Mathematical Society, 1987.

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29

Probability theory subject indexes from Mathematical reviews. Providence, R.I: American Mathematical Society, 1987.

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30

Time Series Clustering and Classification. Chapman and Hall/CRC, 2019.

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31

Maharaj, Elizabeth Ann, Pierpaolo D'Urso, and Jorge Caiado. Time Series Clustering and Classification. Taylor & Francis Group, 2019.

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32

Maharaj, Elizabeth Ann, Pierpaolo D'Urso, and Jorge Caiado. Time Series Clustering and Classification. Taylor & Francis Group, 2019.

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33

Maharaj, Elizabeth Ann, Pierpaolo D'Urso, and Jorge Caiado. Time Series Clustering and Classification. Taylor & Francis Group, 2019.

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34

Maharaj, Elizabeth Ann, Pierpaolo D'Urso, and Jorge Caiado. Time Series Clustering and Classification. Taylor & Francis Group, 2019.

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35

Maharaj, Elizabeth Ann, Jorge Caiado, and Pierpaolo DUrso. Time Series Clustering and Classification. Taylor & Francis Group, 2021.

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36

Bowker-Saur. Class Am/Ax Mathematics, Statistics and Probability (Bliss Bibliographic Classification). 2nd ed. Bowker-Saur, 1993.

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37

Gale, Thomson. Bliss Bibliographic Classification: Class AM/AX: Mathematics, Statistics and Probability. K.G. Saur (An Impint of Walter de Gruyter), 1995.

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38

Construction and assessment of classification rules. Chichester: Wiley, 1997.

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39

Graf, Siegfried, and Harald Luschgy. Foundations of Quantization for Probability Distributions (Lecture Notes in Mathematics). Springer, 2000.

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40

Luschgy, Harald, and Siegfried Graf. Foundations of Quantization for Probability Distributions. Springer London, Limited, 2007.

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41

Jr, Wagner Meira, and Adriano Veloso. Demand-Driven Associative Classification. Springer London, Limited, 2011.

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42

Classification And Multivariate Analysis For Complex Data Structures. Springer, 2011.

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43

Classification and Multivariate Analysis for Complex Data Structures. Springer, 2011.

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44

Classification and regression trees. New York, N.Y: Chapman & Hall, 1993.

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45

Classification and Probabilistic Representation of the Positive Solutions of a Semilinear Elliptic Equation. American Mathematical Society (AMS), 2004.

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46

Classification and Analysis of Low Probability of Intercept Radar Signals Using Image Processing. Storming Media, 2003.

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47

Cronquist, Chapman. Estimation and Classification of Reserves of Crude Oil, Natural Gas, and Condensate. Society of Petroleum Engineers, 2001. http://dx.doi.org/10.2118/9781555630904.

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This book covers all aspects of estimating and classifying reserves of crude oil, natural gas, and condensate attributed to primary recovery mechanisms. Both deterministic and probabilistic procedures are discussed. Reserves definitions for many of the major producing countries are provided, including a comparison of the US Securities and Exchange Commission and Society of Petroleum Engineers–World Petroleum Congress reserves definitions. Case histories illustrate reasons for errors in reserves estimation. Correlation charts and empirical equations to estimate pressure/volume/temperature properties of reservoir fluids are provided in one of several special appendices.

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48

Marriott, F. H. C., and W. J. Krzanowski. Kendall's Advanced Theory of Statistics{: Multivariate Analysis Classification Cov (Kendall, Maurice George//Kendall's Advanced Theory of Statistics 6th ed). Edward Arnold, 1996.

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49

Classification and data analysis: Theory and application : proceedings of the biannual meeting of the Classification Group of Societa Italia di Statistica (SIS), Pescara, July 3-4, 1997. New York: Springer, 1999.

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50

Opitz, Otto, and Italy) Classification Group of Sis Meeting (1997 Pescara. Classification and Data Analysis: Theory and Application : Proceedings of the Biannual Meeting of the Classification Group of Societa Italia Di Statistica ... Data Analysis, and Knowledge Organization). Springer-Verlag Telos, 1999.

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