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DTSTART:20170326T030000
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DTSTART:20171029T020000
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BEGIN:VEVENT
DTSTAMP:20260427T100735Z
UID:59ce013013752852521548@ist.ac.at
DTSTART:20171009T140000
DTEND:20171009T150000
DESCRIPTION:Speaker: Christian Pieringer\nhosted by Maximilian Jösch\nAbst
 ract: Machine learning has been a powerful tool for the automatic classifi
 cation and mining large amount of data. However\, every data domain has ch
 allenges in the way that algorithms can handle it to deliver useful insigh
 ts. Unevenly sampled time series is one kind data that presents challenges
  related to the irregular sampling\, sparsity and noise. These challenges 
 are common among a wide range of research areas\, such as the particular c
 ase of astronomical light curves discussed in this talk.Some of the major 
 tasks in the analysis of these astronomical data sets are feature engineer
 ing\, classification and recently\, visualization of relevant patterns. Fr
 om the feature engineering perspective\, modern methods allow us to discri
 minate possibly relevant patterns among millions of time series directly f
 rom the data in an unsupervised manner. Moreover\, the visualization of th
 ese selected patterns is further designed to aid experts in the identifica
 tion and understanding of the complex dynamics in these astronomical time 
 series. This talk explains the application of dictionary-based methods on 
 coding relevant parts of unevenly sampled time series and the use of this 
 approach on visualization.
LOCATION:Mondi Seminar Room 1\, Central Building\, ISTA
ORGANIZER:rsix@ist.ac.at
SUMMARY:Christian Pieringer: Pattern recognition and visualization in uneve
 nly samples time-series
URL:https://talks-calendar.ista.ac.at/events/870
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