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019 _a1089961074
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_a1229491497
_a1485308216
020 _a9780128146248
_q(electronic bk.)
020 _a0128146249
_q(electronic bk.)
020 _z9780128146231
020 _z0128146230
035 _a(OCoLC)1089804692
_z(OCoLC)1089961074
_z(OCoLC)1104211997
_z(OCoLC)1229491497
_z(OCoLC)1485308216
050 4 _aQA76.9.D343
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082 0 4 _a006.3/12
_223
100 1 _aSimske, Steven J.,
_eauthor.
_1https://id.oclc.org/worldcat/entity/E39PCjwJVJXHYDHYtwtX9pWbh3
_934332
245 1 0 _aMeta-analytics :
_bconsensus approaches and system patterns for data analysis /
_cSteven Simske.
264 1 _aCambridge, MA :
_bMorgan Kaufmann, an imprint of Elsevier,
_c2019.
300 _a1 online resource
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
500 _aIncludes index.
588 0 _aOnline resource; title from PDF title page (ScienceDirect, viewed March 19, 2019).
504 _aIncludes bibliographical references and index.
505 0 _aGround truthing -- Experiment design -- Meta-Analytic design patterns -- Sensitivity analysis and big system engineering -- Multi-path predictive selection -- Modeling and model fitting: including Antibody model, stem-differentiated cell model, and chemical, physical and environmental models for greater diversity in form -- Synonym-antonym and Reinforce-Void patterns and their value in data consensus, data anonymization, and data normalization -- Meta-analytics as analytics around analytics (functional metrics, entropy, EM). Ingesting statistical approaches for specific domains and generalizing them for data hybrid systems -- System design optimization (entropy, error variance, coupling minimization F-score) -- Aleatory techniques/expert system techniques ... tie to ground truthing and error testing -- Applications: machine translation, robotics, biological and social sciences, medical and healthcare informatics, economics, business and finance -- Discussion and Conclusions, and the Future of Data.
520 _aWe live in a world in which huge volumes of data are being collected. The machine intelligence community has been very successful in turning this data into information. Taking the power of hybrid architectures as a starting point, analytics approaches can be upgraded. Meta-Analytics supplies an exhaustive set of patterns for data science to use on any machine learning based data analysis task. The book virtually ensures that at least one pattern will lead to better overall system behaviour than the use of traditional analytics approaches alone. The book is 'meta' to analytics, and so covers general analytics in sufficient detail for the reader to engage with and understand hybrid or meta- approaches. It allows a relative novice to quickly achieve high-level competency. The title has relevance to machine translation, robotics, biological and social sciences, medical and healthcare informatics, economics, business and finance. The analytics can be applied to predictive algorithms for everyone from police departments to sports analysts -- Provided by publisher.
650 0 _aData mining.
650 0 _aMachine learning.
758 _ihas work:
_aMeta-analytics (Text)
_1https://id.oclc.org/worldcat/entity/E39PCGxCdxHWBMrbcFqtFCHFqP
_4https://id.oclc.org/worldcat/ontology/hasWork
776 0 8 _iPrint version:
_aSimske, Steven J.
_tMeta-analytics.
_dCambridge, MA : Morgan Kaufmann, an imprint of Elsevier, 2019
_z0128146230
_z9780128146231
_w(OCoLC)1077575519
856 4 0 _3ScienceDirect
_uhttps://www.sciencedirect.com/science/book/9780128146231
999 _c216521
_d216521