MARC Record from marc_columbia

LEADER: 01903cam a2200325Mi 4500
001 13039679
005 20180319132049.0
008 171124s2017 dcud 000 0 eng d
020 $a0309465737
020 $a9780309465731
035 $a(OCoLC)on1012807602
035 $a(OCoLC)1012807602
035 $a(NNC)13039679
040 $aYDX$beng$erda$cYDX$dNRC
050 4 $aQA276.4$b.C43 2017
245 00 $aChallenges in machine generation of analytic products from multi-source data :$bproceedings of a workshop.
264 1 $aWashington, D.C. :$bNational Academies Press,$c2017.
264 4 $c©2017
300 $ax, 59 pages :$billustrations, charts ;$c28 cm.
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
338 $avolume$bnc$2rdacarrier
520 $aThe Intelligence Community Studies Board of the National Academies of Sciences, Engineering, and Medicine convened a workshop on August 9-10, 2017 to examine challenges in machine generation of analytic products from multi-source data. Workshop speakers and participants discussed research challenges related to machine-based methods for generating analytic products and for automating the evaluation of these products, with special attention to learning from small data, using multi-source data, adversarial learning, and understanding the human-machine relationship. This publication summarizes the presentations and discussions from the workshop.
650 0 $aMathematical statistics$xData processing.
650 0 $aStatistics.
650 0 $aSocial sciences$xStatistical methods.
700 1 $aCasola, Linda,$erapporteur.
710 2 $aNational Academies of Sciences, Engineering, and Medicine (U.S.)
776 08 $iOnline version :$tChallenges in Machine Generation of Analytic Products from Multi-Source Data.$d[S.l.] : [s.n.], 2017$z9780309465731$w(OCoLC)1016344777
852 00 $boff,sci$hQA276.4$i.C424 2017g