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Books on the topic 'Data extractions'

Author: Grafiati

Published: 25 June 2021

Last updated: 1 February 2022

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1

Frank, Lemke, ed. Self-organising data mining: Extracting knowledge from data. Dresden [u.a.]: J.-A. Müller, 2000.

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2

Developments in data extraction, management, and analysis. Hershey, PA: Information Science Reference, 2012.

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3

Mendelssohn, Roy. Comprehensive Ocean Data Set Extraction: User's guide. [La Jolla, Calif.]: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Southwest Fisheries Science Center, 1996.

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4

Paolo, Coletti, ed. Information extraction in finance. Southampton: WIT Press, 2008.

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5

Kipps, James R. Extracting tactical data from operation orders. Santa Monica, CA: Rand, 1992.

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6

Ouyahia, Lamine. Extracting data from several web sites. Oxford: Oxford Brookes University, 2002.

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7

Zhang, Wei Emma, and Quan Z. Sheng. Managing Data From Knowledge Bases: Querying and Extraction. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94935-2.

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8

Smedley, Ruth. Extracting hospital inpatient data for health boards. [S.l: The Author], 2003.

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9

Ward, Thomas E. Kinetic data extraction and analysis system for human gait. Dublin: University College Dublin, 1996.

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10

Liu, Huan. Feature Extraction, Construction and Selection: A Data Mining Perspective. Boston, MA: Springer US, 1998.

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11

Lee, Chulhee. Feature extraction and classification algorithms for high dimensional data. West Lafayette, Ind: School of Electrical Engineering, Purdue University, 1993.

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12

Bonneau, Georges-Pierre, Thomas Ertl, and Gregory M. Nielson, eds. Scientific Visualization: The Visual Extraction of Knowledge from Data. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-30790-7.

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13

1964-, Palade Vasile, ed. Adaptive web sites: A knowledge extraction from web data approach. Amsterdam: IOS Press, 2008.

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14

Iliff, Kenneth W. Extraction of stability and control derivatives from Orbiter flight data. [Washington, DC]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1993.

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15

Bock, Hans-Hermann. Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000.

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16

Kimball, Ralph. The data warehouse ETL toolkit: Practical techniques for extracting, cleaning, conforming, and delivering data. Indianapolis, IN: Wiley, 2004.

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17

Nerozzi, Andrea. Towards BH-NS merger simulations: Initial data, evolution and wave extraction. Portsmouth: University of Portsmouth, 2004.

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18

Martin, Golz, Kuh Anthony, Obradovic Dragan, Tanaka Toshihisa, and SpringerLink (Online service), eds. Signal Processing Techniques for Knowledge Extraction and Information Fusion. Boston, MA: Springer Science+Business Media, LLC, 2008.

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19

Regoczei, Stephen. On 'Extracting knowledge from text': Modelling the architecture of language users. Toronto: Computer Systems Research Institute, University of Toronto, 1989.

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20

Shahrestanaky, Amirmohamad Keyvanloo. Extracting Ramachandran torsional angle distributions from 2D NMR data using Tikhonov regularization. St. Catharines, Ont: Brock University, Dept. of Physics, 2005.

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21

Petrucci, Alessandra, and Rosanna Verde, eds. SIS 2017. Statistics and Data Science: new challenges, new generations. Florence: Firenze University Press, 2017. http://dx.doi.org/10.36253/978-88-6453-521-0.

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The 2017 SIS Conference aims to highlight the crucial role of the Statistics in Data Science. In this new domain of ‘meaning’ extracted from the data, the increasing amount of produced and available data in databases, nowadays, has brought new challenges. That involves different fields of statistics, machine learning, information and computer science, optimization, pattern recognition. These afford together a considerable contribute in the analysis of ‘Big data’, open data, relational and complex data, structured and no-structured. The interest is to collect the contributes which provide from the different domains of Statistics, in the high dimensional data quality validation, sampling extraction, dimensional reduction, pattern selection, data modelling, testing hypotheses and confirming conclusions drawn from the data.

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22

Conference on Knowledge Extraction and Symbolic Data Analysis (1998 Luxembourg). Proceedings of the Conference on Knowledge Extraction and Symbolic Data Analysis: KESDA '98, Luxembourg, 27 to 28 April 1998. Luxembourg: Office for Official Publications of the European Communities, 1999.

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23

Meloun, M. Computation of solution equilibria: A guide to methods in potentiometry, extraction, and spectrophotometry. Edited by Havel J and Högfeldt Erik 1924-. Chichester, England: E. Horwood, 1988.

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24

Pitre, Richard. A boundary scattering strength extraction algorithm for the analysis of long-range reverberation data. Washington, DC: Naval Research Laboratory, 1991.

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25

Trigueiros, Duarte. Neural network based methods in the extraction of knowledge from accounting and financial data. Norwich: University of East Anglia, 1991.

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26

Ballard, John Michael. Generic computational algorithms for extracting 3D machinability data from a wireframe CAD system. [Derby: University of Derby], 1999.

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27

Canada. Dept. of Fisheries and Oceans. The MRP-report program: A data extraction and reporting tool for the mark recovery program database. S.l: s.n, 1988.

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28

Li, Ying. Video Content Analysis Using Multimodal Information: For Movie Content Extraction, Indexing and Representation. Boston, MA: Springer US, 2003.

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29

Multimedia information extraction: Advances in video, audio, and imagery analysis for search, data mining, surveillance, and authoring. Hoboken, N.J: Wiley, 2012.

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30

Golubkina, Nadezhda, Elena Kekina, Anna Molchanova, and Sergey Nadezhkin. Antioxidants of plants and methods of their definition. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1045420.

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The monograph presents the most simple and widely used methods for determining the most important of plant antioxidants: vitamin C, polyphenols, carotenoids, capsaicin, and belinovich photosynthetic pigments, flavonoids, anthocyanins, alkaloids, tannins, and minerals antioxidant: selenium and iodine. Special attention is paid to methods of extraction of antioxidants, providing maximum extraction of antioxidants from plant material, and the correct selection of the most appropriate method of analysis of one or another component. Provides detailed information developed by the authors method of using thin layer chromatography to assess the carotenoid composition of tomatoes and peppers. The data presented here include results of research conducted on the basis of FICO, as well as the latest developments of foreign scientists devoted to natural antioxidants and methods of their determination. Presented in this monograph methodology was successfully tested in the laboratory and analytical Department of PNCO in 2012-2018. For students and teachers and all interested in horticulture and agriculture.

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31

Koutsojannis, Constantinos M. Medical Knowledge Extraction from Big Data. Nova Science Publishers, Incorporated, 2020.

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32

Chemometrics: Data Driven Extraction for Science. Wiley & Sons, Incorporated, John, 2018.

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33

Chemometrics: Data Driven Extraction for Science. Wiley, 2018.

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34

Brereton, Richard G. Chemometrics: Data Driven Extraction for Science. Wiley & Sons, Incorporated, John, 2018.

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35

Koutsojannis, Constantinos M. Medical Knowledge Extraction from Big Data. Nova Science Publishers, Incorporated, 2020.

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36

Ertl, Thomas, Gregory M. Nielson, and Georges-Pierre Bonneau. Scientific Visualization : The Visual Extraction of Knowledge from Data: The Visual Extraction of Knowledge from Data. Springer, 2010.

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37

Karjalainen, U. P., and E. J. Karjalainen. Spectrum Extraction from Hyphenated Data (CD-ROM). Elsevier Science Ltd, 1996.

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38

Church of Jesus Christ of Latter-Day Saints., ed. Family record extraction. [Salt Lake City]: Church of Jesus Christ of Latter-day Saints, 1993.

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39

Wu, Hulin, Jose Miguel Yamal, Ashraf Yaseen, and Vahed Maroufy. Statistics and Machine Learning Methods for EHR Data: From Data Extraction to Data Analytics. Taylor & Francis Group, 2020.

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40

Statistics and Machine Learning Methods for EHR Data: From Data Extraction to Data Analytics. Taylor & Francis Group, 2020.

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41

Wu, Hulin, Jose Miguel Yamal, Ashraf Yaseen, and Vahed Maroufy. Statistics and Machine Learning Methods for EHR Data: From Data Extraction to Data Analytics. Taylor & Francis Group, 2020.

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42

Wu, Hulin, Jose Miguel Yamal, Ashraf Yaseen, and Vahed Maroufy. Statistics and Machine Learning Methods for EHR Data: From Data Extraction to Data Analytics. Taylor & Francis Group, 2020.

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43

Sheng, Quan Z., and Wei Emma Zhang. Managing Data From Knowledge Bases: Querying and Extraction. Springer, 2019.

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44

Sheng, Quan Z., and Wei Emma Zhang. Managing Data From Knowledge Bases: Querying and Extraction. Springer, 2018.

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45

Canadian Institute of Chartered Accountants., ed. Interactive Data Extraction and Analysis, version 4.0: User's manual. Toronto: Canadian Institute of Chartered Accountants, 1990.

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46

Feature Extraction, Construction and Selection: A Data Mining Perspective. Springer, 2011.

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47

G, Planet Walter, and United States. National Environmental Satellite, Data, and Information Service, eds. Data extraction and calibration of TIROS-N/NOAA radiometers. Washington, D.C: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 1988.

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48

1958-, Liu Huan, and Motoda Hiroshi, eds. Feature extraction, construction and selection: A data mining perspective. Boston: Kluwer Academic, 1998.

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49

Boudreau, Joseph F., and Eric S. Swanson. Data modeling. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198708636.003.0016.

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A variety of techniques for extracting information from data are presented, from pedestrian approaches such as the centuries old linear least-squares fit, to elegant binned and unbinned likelihood fits. A treatment of statistical combination of data leads to an introduction to the powerful Kalman filter approach, used to determine optimal estimates of deterministic-stochastic systems. In experimental physics the Kalman filter is used estimate trajectories from data, but it also finds applications in industrial process control, and in the aeronautics and robots industries. These techniques typically rely on either analytic or numerical optimization of an objective function. Orthogonal series density estimation, a Fourier technique, is also discussed.

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50

Chennubhotla, Srinivas Chakra. Spectral methods for multi-scale feature extraction and data clustering. 2004.

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