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EFFECTS: Explorable and Explainable Feature Extraction Fram..:
, In:
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
,
Ikar, Ido
;
Somech, Amit
- p. 5061-5065 , 2023
Link:
https://dl.acm.org/doi/10.1145/3583780.3614740
RT T1 <cTitle>Proceedings of the 32nd ACM International Conference on Information and Knowledge Management</cTitle>: T1 <Title>EFFECTS: Explorable and Explainable Feature Extraction Framework for Multivariate Time-Series Classification</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3614740&Exemplar=1&LAN=DE A1 Ikar, Ido A1 Somech, Amit PB ACM YR 2023 K1 explainability K1 exploration of time series data K1 Mathematics of computing K1 Probability and statistics K1 Statistical paradigms K1 Time series analysis K1 Exploratory data analysis K1 Information systems K1 Data management systems K1 Database design and models K1 Data model extensions K1 Temporal data SP 5061 OP 5065 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3583780.3614740 DO https://dl.acm.org/doi/10.1145/3583780.3614740 SF ELIB - SuUB Bremen
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