Density-Approximating Neural Network Models for Anomaly Detection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61384399%3A31160%2F18%3A00053456" target="_blank" >RIV/61384399:31160/18:00053456 - isvavai.cz</a>
Result on the web
<a href="https://www.andrew.cmu.edu/user/lakoglu/odd/accepted_papers/ODD_v50_paper_19.pdf" target="_blank" >https://www.andrew.cmu.edu/user/lakoglu/odd/accepted_papers/ODD_v50_paper_19.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Density-Approximating Neural Network Models for Anomaly Detection
Original language description
Main topics of the document: anomaly detection; neural network; nearest neighbor
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of ACM SIGKDD
ISBN
978-1-4503-2138-9
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
1-8
Publisher name
The Association for Computing Machinery (ACM) - Special Interest Group on Knowledge Discovery and Data Mining
Place of publication
London
Event location
London
Event date
Aug 20, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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