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02542nam a22005415i 4500 |
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978-3-319-69014-8 |
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20171227233927.0 |
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171227s2017 gw | s |||| 0|eng d |
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|a 9783319690148
|9 978-3-319-69014-8
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|a 10.1007/978-3-319-69014-8
|2 doi
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|d GrThAP
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|a HD72-88
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|a BUS068000
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|a 338.9
|2 23
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|a Basuchoudhary, Atin.
|e author.
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|a Machine-learning Techniques in Economics
|h [electronic resource] :
|b New Tools for Predicting Economic Growth /
|c by Atin Basuchoudhary, James T. Bang, Tinni Sen.
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|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2017.
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|a VI, 94 p. 20 illus., 19 illus. in color.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a text file
|b PDF
|2 rda
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|a SpringerBriefs in Economics,
|x 2191-5504
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|a Why this Book? -- Data, Variables, and Their Sources -- Methodology -- Predicting Economic Growth: A First Look -- Predicting Economic Growth: Which Variables Matter? -- Predicting Recessions: What We Learn from Widening the Goalposts -- Epilogue.
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|a This book develops a machine-learning framework for predicting economic growth. It can also be considered as a primer for using machine learning (also known as data mining or data analytics) to answer economic questions. While machine learning itself is not a new idea, advances in computing technology combined with a dawning realization of its applicability to economic questions makes it a new tool for economists. .
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|a Data mining.
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|a Application software.
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|a Econometrics.
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|a Game theory.
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|a Economic growth.
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|a Economics.
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|a Economic Growth.
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|a Econometrics.
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|a Data Mining and Knowledge Discovery.
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|a Game Theory.
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|a Computer Appl. in Social and Behavioral Sciences.
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700 |
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|a Bang, James T.
|e author.
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|a Sen, Tinni.
|e author.
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|a SpringerLink (Online service)
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|t Springer eBooks
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776 |
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|i Printed edition:
|z 9783319690131
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830 |
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|a SpringerBriefs in Economics,
|x 2191-5504
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856 |
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|u http://dx.doi.org/10.1007/978-3-319-69014-8
|z Full Text via HEAL-Link
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912 |
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|a ZDB-2-ECF
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950 |
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|a Economics and Finance (Springer-41170)
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