A Computational Framework for Interpretable Anomaly Detection and Classification of Multivariate Time Series with Application to Human Gait Data Analysis-Reference-Cited by-同舟云学术

A Computational Framework for Interpretable Anomaly Detection and Classification of Multivariate Time Series with Application to Human Gait Data Analysis

Published:2019 Issue: Volume: Page:132-147
ISSN:0302-9743
Container-title:Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems
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Author:

Ramirez Erica,Wimmer Markus,Atzmueller Martin

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-37446-4_11

Reference33 articles.

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3. Atzmueller, M.: Onto explicative data mining: exploratory, interpretable and explainable analysis. In: Proceedings of the DBDBD 2017. TU Eindhoven, Netherlands (2017)

4. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence);M Atzmueller,2018

5. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence);M Atzmueller,2005

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