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Books on the topic 'Classification methods'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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1

Pattern classification using ensemble methods. Singapore: World Scientific, 2010.

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2

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

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3

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

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4

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

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5

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

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6

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

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7

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

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8

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

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9

1929-, Agrawal S. P., ed. Book numbers: Some Indian methods. New Delhi: Concept Pub. Co., 1990.

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10

Particle size analysis: Classification and sedimentation methods. London: Chapman & Hall, 1994.

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11

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

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12

Pattern classification: Neuro-fuzzy methods and their comparison. London: Springer, 2001.

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13

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

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14

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

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15

Moths: Types, ecological significance, and control methods. New York: Nova Science Publishers, 2012.

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16

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

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17

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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18

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

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19

A, Cockayne, ed. Molecular methods for microbial identification and typing. London: Chapman & Hall, 1993.

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20

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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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

Okun, Oleg. Feature selection and ensemble methods for bioinformatics: Algorithmic classification and implementations. Hershey PA: Medical Information Science Reference, 2011.

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23

Bock, Hans-Hermann. Classification, Data Analysis, and Knowledge Organization: Models and Methods with Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991.

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24

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

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25

Ross, Jeremy. Acupuncture point combinations: The key to clinical success. Edinburgh: Churchill Livingstone, 1995.

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26

Acupuncture point combinations: The key to clinical success. Edinburgh: Churchill Livingstone, 1995.

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27

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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28

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

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29

Riesen, Kaspar. Graph classification and clustering based on vector space embedding. New Jersey: World Scientific, 2010.

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30

Service, Canadian Parks. Canadian Parks Service classification system for historical collections. Ottawa, Ont: National Historic Sites, Parks Service, Environment Canada, 1992.

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31

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

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32

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

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33

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 kernel rules. It also describes classification based on partial least squares, classification based on reproducing kernels, and depth-based classification. Finally, it examines unsupervised classification methods, focusing on K-means for functional data, K-means for data in a Hilbert space, and impartial trimmed K-means for functional data. Some practical issues, in particular real-data examples and simulations, are reviewed and some selected proofs are given.

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34

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

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35

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

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36

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

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37

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

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38

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

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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

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

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41

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

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42

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

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43

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

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44

Cinahl. Classification of Nursing Diagnoses Nanda (Classification of Nursing Diagnosis). Cinahl Information Systems, 2000.

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45

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

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46

Alfaro, Esteban, Mat�as G�mez, and Noelia Garc�a. Ensemble Classification Methods with Applications in R. Wiley & Sons, Incorporated, John, 2018.

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47

Alfaro, Esteban, Mat�as G�mez, and Noelia Garc�a. Ensemble Classification Methods with Applications in R. Wiley & Sons, Incorporated, John, 2018.

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48

Zopounidis, C., and M. Doumpos. Multicriteria Decision Aid Classification Methods (Applied Optimization). Springer, 2002.

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49

Rokach, Lior. Ensemble Learning: Pattern Classification Using Ensemble Methods. World Scientific Publishing Co Pte Ltd, 2019.

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50

Colwell, Rita R. Methods in Microbiology: Current Methods for Classification and Identification of Microorganisms (Methods in Microbiology). Academic Press, 1988.

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