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Books on the topic 'Classification methods'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Holeňa, Martin, Petr Pulc, and Martin Kopp. Classification Methods for Internet Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36962-0.

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2

Doumpos, Michael. Multicriteria decision aid classification methods. Kluwer Academic Publishers, 2002.

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3

Constantin, Zopounidis, ed. Multicriteria decision aid classification methods. Kluwer Academic Publishers, 2002.

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4

Hayashi, Chikio, Keiji Yajima, Hans-Hermann Bock, Noboru Ohsumi, Yutaka Tanaka, and Yasumasa Baba, eds. Data Science, Classification, and Related Methods. Springer Japan, 1998. http://dx.doi.org/10.1007/978-4-431-65950-1.

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5

Alfaro, Esteban, Matías Gámez, and Noelia García, eds. Ensemble Classification Methods with Applicationsin R. John Wiley & Sons, Ltd, 2018. http://dx.doi.org/10.1002/9781119421566.

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6

Kiers, Henk A. L., Jean-Paul Rasson, Patrick J. F. Groenen, and Martin Schader, eds. Data Analysis, Classification, and Related Methods. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-59789-3.

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7

L, Kiers Henk A., and International Federation of Classification Societies. Conference, eds. Data analysis, classification, and related methods. Springer, 2000.

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8

M, Mather Paul, ed. Classification methods for remotely sensed data. 2nd ed. CRC Press, 2009.

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9

Bernhardt, Claus. Particle Size Analysis: Classification and sedimentation methods. Springer Netherlands, 1994.

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10

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

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11

1968-, Malhotra Raman, ed. Cataract: Assessment, classification, and management. Elsevier, Butterworth Heinemann, 2007.

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12

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

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13

1934-, Colwell Rita R., and Grigorova R, eds. Current methods for classification and identification of microorganisms. Academic Press, 1987.

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14

Summerfield, Patricia. Historical collections classification scheme for small museums. Museums Association of Australia, W.A. Branch, 1988.

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15

Jung, Reinhard. Analysing pottery: Processing, classification, publication. Comenius University in Bratislava, 2010.

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16

A, Samson Robert, and Pitt John I, eds. Integration of modern taxonomic methods for penicillium and aspergillus classification. Harwood Academicn Publishers, 2000.

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17

Organisation for Economic Co-operation and Development., ed. Occupational classification (ISCO-88): Concepts, methods, reliability, validity, and cross-national comparability. OECD, 1997.

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18

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

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19

California. Dept. of Corrections., ed. Report to the Legislature--Classification Score System simulation study. The Department, 1998.

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20

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

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21

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

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22

B, Radzhabova M., ed. Klassifika͡tsi͡ia khlopka-syr͡tsa po statisticheskomu algoritmu. V͡TS AN SSSR, 1990.

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23

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

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24

J, Rantz Marilyn, LeMone Priscilla, North American Nursing Diagnosis Association., and National Conference on the Classification of Nursing Diagnoses (13th : 1998 : St. Louis, Mo.), eds. Classification of nursing diagnoses: Proceedings of the thirteenth conference. CINAHL Information Systems, 1999.

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25

J, Rantz Marilyn, LeMone Priscilla, North American Nursing Diagnosis Association., and NANDA Conference on the Classification of Nursing Diagnoses (11th : 1994 : Nashville, Tenn.), eds. Classification of nursing diagnoses: Proceedings of the eleventh conference. CINAHL Information Systems, 1995.

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26

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

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27

Malmquist, Gunnar. Chemometrics for characterization, classification and prediction in chromatography. Acta Universitatis Upsaliensis, 1993.

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28

Frank, Höppner, ed. Fuzzy cluster analysis: Methods for classification, data analysis, and image recognition. J. Wiley, 1999.

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29

Laflin, Susan. Computer Methods of Classification. Lulu Press, Inc., 2014.

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30

Holeňa, Martin, Petr Pulc, and Martin Kopp. Classification Methods for Internet Applications. Springer, 2020.

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31

Baillo, Amparo, Antonio Cuevas, and Ricardo Fraiman. Classification methods for functional data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.10.

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Abstract:

This article reviews the literature concerning supervised and unsupervised classification of functional data. It first explains the meaning of unsupervised classification vs. supervised classification before discussing the supervised classification problem in the infinite-dimensional case, showing that its formal statement generally coincides with that of discriminant analysis in the classical multivariate case. It then considers the optimal classifier and plug-in rules, empirical risk and empirical minimization rules, linear discrimination rules, the k nearest neighbor (k-NN) method, and kern

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32

Multicriteria Decision Aid Classification Methods. Kluwer Academic Publishers, 2004. http://dx.doi.org/10.1007/b101986.

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33

Holeňa, Martin, Petr Pulc, and Martin Kopp. Classification Methods for Internet Applications. Springer International Publishing AG, 2021.

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34

Pattern Classification Using Ensemble Methods. World Scientific Publishing Co Pte Ltd, 2009.

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35

Zopounidis, Constantin, and Michael Doumpos. Multicriteria Decision Aid Classification Methods. Springer, 2010.

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36

Rokach, Lior. Pattern Classification Using Ensemble Methods. World Scientific Publishing Co Pte Ltd, 2009.

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37

Pattern classification using ensemble methods. World Scientific, 2010.

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38

Mather, Paul, and Brandt Tso. Classification Methods for Remotely Sensed Data. Taylor & Francis Group, 2016.

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39

Mather, Paul, and Brandt Tso. Classification Methods for Remotely Sensed Data. Taylor & Francis Group, 2016.

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40

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

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41

Normolle, Daniel Paul. Comparing the performance of classification methods. 1987.

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42

Buza, Krisztian. Fusion Methods for Time-Series Classification. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2011.

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43

Data Analysis, Classification, and Related Methods. Springer, 2012.

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44

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

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45

Mather, Paul, and Brandt Tso. Classification Methods for Remotely Sensed Data. Taylor & Francis Group, 2001.

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46

Mather, Paul, and Brandt Tso. Classification Methods for Remote Sensed Data. CRC, 2001.

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47

Randomized Ensemble Methods for Classification Trees. Storming Media, 2002.

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48

Schader, Martin, Jean-Paul Rasson, Patrick J. F. Groenen, and Henk A. L. Kiers. Data Analysis, Classification, and Related Methods. Springer London, Limited, 2012.

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49

Mather, Paul, and Brandt Tso. Classification Methods for Remotely Sensed Data. Taylor & Francis Group, 2003.

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50

Mather, Paul, and Brandt Tso. Classification Methods for Remote Sensed Data. CRC, 2001.

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