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Books on the topic 'Datasety'

Author: Grafiati

Published: 28 June 2021

Last updated: 7 February 2022

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1

1942-, Ullman Jeffrey D., ed. Mining of massive datasets. Cambridge: Cambridge University Press, 2012.

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2

Hutchinson, T. P. The datasets from CBDEA. Adelaide: Rumsby Scientific Publishing, 1993.

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3

Milanović, Branko. Dataset - racial tension, volume 6. [Washington, D.C: World Bank, 2005.

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4

Quiñonero-Candela, Joaquin. Dataset shift in machine learning. Cambridge, MA: MIT Press, 2009.

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5

Rymer, Thomas E. Data management for large volume datasets. S.l: s.n, 1986.

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6

Drechsler, Jörg. Synthetic Datasets for Statistical Disclosure Control. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0326-5.

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7

Ravindra Babu, T., M. Narasimha Murty, and S. V. Subrahmanya. Compression Schemes for Mining Large Datasets. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5607-9.

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8

Scanlon, S. The UK's networked dataset revolution continues. London: Library Information Technology Centre, 1993.

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9

Mayeul, Kauffmann, and North Atlantic Treaty Organization. Public Diplomacy Division., eds. Building and using datasets on armed conflicts. Amsterdam, Netherlands: IOS Press, 2008.

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10

Johnson, Lisa Kaye. Computer analysis of remote sensing and geologic datasets. Washington: Washington State University Department of Geology, 1988.

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11

Understanding complex datasets: Data mining with matrix decompositions. Boca Raton: CRC Press, 2007.

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12

United States. Bureau of Justice Statistics., ed. Linking uniform crime reporting data to other datasets. [Washington, D.C.]: U.S. Dept. of Justice, Office of Justice Programs, Bureau of Justice Statistics, 2001.

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13

McMullan, Trevor. General information on national and international dataset services. [Londonderry]: University of Ulster Computer Services, 1992.

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14

Edmunds, Huw. Further developments in online access to large cartographic datasets. Cardiff: Wales and South West Regional Research Laboratory, 1989.

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15

Complex datasets and inverse problems: Tomography, networks, and beyond. Beachwood, Ohio: Institute of Mathematical Statistics, 2007.

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16

Drechsler, Jörg. Synthetic datasets for statistical disclosure control: Theory and implementation. New York: Springer, 2011.

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17

Emetere, Moses Eterigho. Environmental Modeling Using Satellite Imaging and Dataset Re-processing. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13405-1.

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18

Boleneus, D. E. Geologic datasets for weights of evidence analysis in northeast Washington. [Reston, Va: U.S. Dept. of the Interior, U.S. Geological Survey], 2000.

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19

Martin, Ralf. Globalisation, ICT and the nitty gritty of plant level datasets. London: Centre for Economic Performance, London School of Economics and Political Science, 2004.

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20

Boleneus, D. E. Geologic datasets for weights of evidence analysis in northeast Washington. [Menlo Park, CA]: U.S. Geological Survey, 1999.

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21

Deichmann, Uwe. Global digital datasets for land degradation studies: A GIS approach. Nairobi: Global Environment Monitoring System, United Nations Environment Programme, 1991.

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22

Straus, David M. Five year global dataset: NMC operational analyses, 1978 to 1982. Washington, D.C: National Aeronautics and Space Administration, Scientific and Technical Information Office, 1987.

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23

Strauss, David. Five Year Global Dataset: NMC Operational Analyses (1979 to 1982). Washington, D.C: NASA, Scientific and Technical information Office, 1987.

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24

Chesher, Andrew. Review of the literature on the statistical properties of linked datasets. [England]: Dept. of Trade, 2006.

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25

Harding, A. E. Anal ysis of multiple geological datasets from the English Lake District. Farnborough: National Remote Sensing Centre, Space Department, Royal Aerospace Establishment, 1989.

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26

Openshaw, Stan. Making sense of large flow datasets for marketing and other purposes. Newcastle upon Tyne: NorthEast Regional Research Laboratory, 1988.

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27

Wagner, Wolfgang. Parliamentary war powers around the world, 1989-2004: A new dataset. Geneva: Geneva Centre for the Democratic Control of Armed Forces (DCAF), 2010.

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28

Wright, Peter. Union membership and coverage: An econometric study using the SCELI dataset. Nottingham: University of Nottingham, 1994.

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29

Li, Kang. Application of various modelling techniques to analyse housing condition survey dataset. [s.l: The Author], 2002.

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30

Faust, Jon. Comparing Greenbook and reduced form forecasts using a large realtime dataset. Cambridge, Mass: National Bureau of Economic Research, 2007.

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31

Faust, Jon. Comparing greenbook and reduced form forecasts using a large realtime dataset. Cambridge, MA: National Bureau of Economic Research, 2007.

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32

Skillicorn, David. Understanding Complex Datasets. Chapman and Hall/CRC, 2007. http://dx.doi.org/10.1201/9781584888338.

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33

Geological Survey (U.S.), ed. National elevation dataset. [Reston, Va.?: U.S. Dept. of the Interior, U.S. Geological Survey, 1999.

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34

Dataset: Economics & business. Winchester: Trigon, 1991.

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35

Dataset: Economics & business. Winchester: Trigon, 1994.

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36

Malhotra, Naresh K. Dataset CD-ROM. Pearson Education, Limited, 2003.

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37

García Bacete, Francisco. Dataset PSI.2020.12. Universitat Jaume I, 2020. http://dx.doi.org/10.6035/psi.2020.12.

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38

MINING OF MASSIVE DATASETS. CAMBRIDGE UNIVERSITY PRESS, 2017.

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39

Anand Rajaraman Jeffrey David Ullman . Mining of Massive Datasets. Cambridge University, 2014.

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40

Graphics of Large Datasets. New York, NY: Springer New York, 2006. http://dx.doi.org/10.1007/0-387-37977-0.

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41

Geological Survey (U.S.), ed. National land cover dataset. [Reston, Va.?]: U.S. Dept. of the Interior, U.S. Geological Survey, 2000.

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42

Geological Survey (U.S.), ed. The National Hydrography Dataset. [Reston, Va.?]: U.S. Dept. of the Interior, U.S. Geological Survey, 1999.

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43

Hooghe, Liesbet, Gary Mark, Tobias Lenz, Jeanine Bezuijen, Besir Ceka, Svet Derderyan, Liesbet Hooghe, et al. Constructing the MIA Dataset. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198724490.003.0003.

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Abstract:

Chapter Three introduces the Measure of International Authority (MIA) index on delegation and pooling. The first two sections describe how the authors aggregate scores for individual IO bodies at particular stages of decision making in particular decision areas to estimate delegation and pooling at the level of an international organization. In short, it explains the algorithm that produces delegation and pooling scores. The third section presents descriptive statistics comparing delegation and pooling over time, across IOs, and across decision areas. The chapter concludes with tables that summarize the extent of delegation and pooling, in the aggregate and by decision area, for each of seventy-six IOs in the MIA dataset. The scores tap annual variation from 1950 (or date of IO creation) to 2010 (or date of IO death).

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44

Initial CAALS device capability dataset. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1998.

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45

Economics and Business Dataset 1998. Winchester: Trigon, 1998.

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46

Quiñonero-Candela, Joaquin, Masashi Sugiyama, Anton Schwaighofer, and Neil D. Lawrence, eds. Dataset Shift in Machine Learning. The MIT Press, 2008. http://dx.doi.org/10.7551/mitpress/9780262170055.001.0001.

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47

Dataset, Inc: A Corporate Intrigue. Writer's Showcase Press, 2000.

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48

Analysis of an Intelligence Dataset. MDPI, 2021. http://dx.doi.org/10.3390/books978-3-0365-0041-6.

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49

World Population Prospects 2004: Extended Dataset. United Nations Pubns, 2006.

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50

World Population Prospects 2004: Basic Dataset. United Nations Pubns, 2006.

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