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X-WR-CALDESC:Events for datacraft
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DTSTART;TZID=Europe/Paris:20210924T140000
DTEND;TZID=Europe/Paris:20210924T160000
DTSTAMP:20260405T134824
CREATED:20210702T121450Z
LAST-MODIFIED:20210913T192539Z
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SUMMARY:Multivariate and functional anomaly detection - Part 1
DESCRIPTION:inscription\n			\n				\n				\n				\n				\n				Workshop led by Pavlo Mozharovskyi\, Telecom Paris. \nAnomaly detection (Chandola et al.\, 2009) is a branch of machine learning which aims at identifying observations that exhibit abnormal behavior. Be it measurement errors\, disease development\, severe weather\, production quality default(s) (items) or failed equipment\, financial frauds or crisis events\, their on-time identification\, isolation and explanation constitute an important task in almost any branch of industry and science. \nDuring this workshop\, one of these following topic will be discussed : \n\nthe concept of data depth in both functional and multivariate settings\,\nreview most common notion of the depth function (halfspace (Tukey\, 1975)\, projection (Zuo & Sefling\,2000)\, zonoid (Mosler\, 2002)\, spatial depth (Koltchinskii\, 1997); integrated (Claeskens et al.\, 2014) and curve (Lafaye De Micheaux et al.\, 2020) functional depths\, functional isolation forest Staerman et al. (2019)\nfocus on a number of real-world applications ranging from simulated situations to hurricane tracks and brain imaging.\n\n  \nPlease note that the workshop will be conducted in French or English depending on the participants.
URL:https://datacraft.paris/event/multivariate-and-functional-anomaly-detection-part-1/
LOCATION:datacraft –\, 3 rue Rossini\, 75009 Paris\, France
ORGANIZER;CN="datacraft":MAILTO:contact@datacraft.paris
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