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▼a 9780309496124
▼q (electronic bk.)
▼a 0309496128
▼q (electronic bk.)
▼z 9780309496094
▼q (paperback)
▼z 0309496098
▼q (paperback)
▼a 2264538
▼b (N$T)
▼a (OCoLC)1121628374
▼a N$T
▼b eng
▼e rda
▼e pn
▼c N$T
▼d N$T
▼d CUS
▼d 248023
▼a HV6773.15.C97
▼a Q325.5
▼a 006.31
▼2 23
▼a Casola, Linda Clare,
▼d 1982-,
▼e rapporteur.
▼a Robust machine learning algorithms and systems for detection and mitigation of adversarial attacks and anomalies:
▼b proceedings of a workshop /:
▼c Linda Casola and Dionna Ali, rapporteurs ; Intelligence Community Studies Board ; Computer Science and Telecommunications Board, Division on Engineering and Physical Sciences, the National Academies of Sciences, Engineering, Medicine.
▼a Washington, DC:
▼b the National Academies Press,
▼c [2019].
▼a 1 online resource (xii, 69 pages):
▼b color illustrations.
▼a text
▼b txt
▼2 rdacontent
▼a computer
▼b c
▼2 rdamedia
▼a online resource
▼b cr
▼2 rdacarrier
▼a Includes bibliographical references (pages 53-54).
▼a Introduction -- Plenary session -- Adversarial attacks -- Detection and mitigation of adversarial attacks and anomalies -- Enablers of machine learning algorithms and systems -- Recent trends i machine learning, parts 1 and 2 -- Plenary session -- Recent trends in machine learning, part 3 -- Machine learning systems --References -- Appendixes
▼a "The Intelligence Community Studies Board (ICSB) of the National Academies of Sciences, Engineering, and Medicine convened a workshop on December 11-12, 2018, in Berkeley, California, to discuss robust machine learning algorithms and systems for the detection and mitigation of adversarial attacks and anomalies. This publication summarizes the presentations and discussions from the workshop"--Publisher's description
▼a Online resource; title from PDF title page (National Academies Press, viewed March 26, 2019).
▼a Master record variable field(s) change: 050, 650
▼a Machine learning
▼v Congresses.
▼a Computer algorithms
▼v Congresses.
▼a Cyberterrorism
▼x Prevention
▼v Congresses.
▼a Machine learning.
▼a Computer security.
▼a Computer networks
▼x Security measures.
▼a Ali, Dionna,
▼e rapporteur.
▼a National Academies of Sciences, Engineering, and Medicine (U.S.).
▼b Intelligence Community Studies Board,
▼e issuing body.
▼a National Academies of Sciences, Engineering, and Medicine (U.S.).
▼b Computer Science and Telecommunications Board,
▼e issuing body.
▼a Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies (Workshop)
▼d (2018 :
▼c Berkeley, Ca.),
▼j author.
▼3 EBSCOhost
▼u http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2264538
▼a EBSCOhost
▼b EBSC
▼n 2264538
▼a 강리원
▼a eBook
▼a 92
▼b N$T
| 자료유형 : | eBook |
|---|---|
| ISBN : | 9780309496124 |
| ISBN : | 0309496128 |
| ISBN : | |
| ISBN : | |
| 개인저자 : | Casola, Linda Clare, 1982-, rapporteur. |
| 서명/저자사항 : | Robust machine learning algorithms and systems for detection and mitigation of adversarial attacks and anomalies: proceedings of a workshop /: Linda Casola and Dionna Ali, rapporteurs ; Intelligence Community Studies Board ; Computer Science and Telecommunications Board, Division on Engineering and Physical Sciences, the National Academies of Sciences, Engineering, Medicine. |
| 발행사항 : | Washington, DC: the National Academies Press, [2019]. |
| 형태사항 : | 1 online resource (xii, 69 pages): color illustrations. |
| 서지주기 : | Includes bibliographical references (pages 53-54). |
| 내용주기 : | Introduction -- Plenary session -- Adversarial attacks -- Detection and mitigation of adversarial attacks and anomalies -- Enablers of machine learning algorithms and systems -- Recent trends i machine learning, parts 1 and 2 -- Plenary session -- Recent trends in machine learning, part 3 -- Machine learning systems --References -- Appendixes |
| 요약 : | "The Intelligence Community Studies Board (ICSB) of the National Academies of Sciences, Engineering, and Medicine convened a workshop on December 11-12, 2018, in Berkeley, California, to discuss robust machine learning algorithms and systems for the detection and mitigation of adversarial attacks and anomalies. This publication summarizes the presentations and discussions from the workshop"--Publisher's description |
| 일반주제명 : | Machine learning -- Congresses. -- |
| 일반주제명 : | Computer algorithms -- Congresses. -- |
| 일반주제명 : | Cyberterrorism -- Prevention -- Congresses. -- |
| 일반주제명 : | Machine learning. -- |
| 일반주제명 : | Computer security. -- |
| 일반주제명 : | Computer networks -- Security measures. -- |
| 개인저자 : | Ali, Dionna, rapporteur. |
| 단체저자명 : | National Academies of Sciences, Engineering, and Medicine (U.S.). Intelligence Community Studies Board, issuing body. |
| 단체저자명 : | National Academies of Sciences, Engineering, and Medicine (U.S.). Computer Science and Telecommunications Board, issuing body. |
| 회의명 : | Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies (Workshop) (2018 : Berkeley, Ca.), |
| 언어 | 영어 |
| URL : |
|---|
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