BEGIN:VCALENDAR VERSION:2.0 PRODID:-//jEvents 2.0 for Joomla//EN CALSCALE:GREGORIAN METHOD:PUBLISH BEGIN:VEVENT UID:2256ab4d78e377d08871feb874ea2c13 CATEGORIES:Conferences CREATED:20130819T140313 SUMMARY:Mobile Sensing for Large-Scale Human Behavior Mining LOCATION:SBA Research gGmbH - Wien DESCRIPTION:Katayoun Farrahi - Guest talk "Mobile Sensing for Large-Scale Human Behavio r Mining"\n \n \nMining patterns of human behavior from large-scale mobile phone data has potential to understand certain phenomena in society. The st udy of such human-centric massive datasets requires new mathematical models . In this talk I will discuss a novel probabilistic topic model, the distan t n-gram topic model (DNTM), to address the problem of learning long durati on human location sequences. The DNTM is based on Latent Dirichlet Allocati on (LDA) and is advantageous for mining human behavior patterns for many re asons. This model is evaluated on 2 real mobile phone datasets collected by mobile phone locations, the first considering GPS locations and the second considering cell tower connections. The DNTM is presented as an example of a topic model for mining large-scale data related to human behavior. Exten sions of this work will then be discussed, the first relating to data-drive n mobility modeling for large-scale agent based models and the second relat ing to epidemics.\n X-ALT-DESC;FMTTYPE=text/html:
Katayoun Farrahi - Guest talk "Mobile Sensing for Large-Scale Human Beha vior Mining"
Mining patterns of h uman behavior from large-scale mobile phone data has potential to understan d certain phenomena in society. The study of such human-centric massive dat asets requires new mathematical models. In this talk I will discuss a novel probabilistic topic model, the distant n-gram topic model (DNTM), to addre ss the problem of learning long duration human location sequences. The DNTM is based on Latent Dirichlet Allocation (LDA) and is advantageous for mini ng human behavior patterns for many reasons. This model is evaluated on 2 r eal mobile phone datasets collected by mobile phone locations, the first co nsidering GPS locations and the second considering cell tower connections. The DNTM is presented as an example of a topic model for mining large-scale data related to human behavior. Extensions of this work will then be discu ssed, the first relating to data-driven mobility modeling for large-scale a gent based models and the second relating to epidemics.
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