SSMM: Slotted Symbolic Markov Modeling for classifying variable star signatures

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Ada Coda
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SSMM: Slotted Symbolic Markov Modeling for classifying variable star signatures

Post by Ada Coda » Tue Jul 31, 2018 9:35 pm

SSMM: Slotted Symbolic Markov Modeling for classifying variable star signatures

Abstract: SSMM (Slotted Symbolic Markov Modeling) reduces time-domain stellar variable observations to classify stellar variables. The method can be applied to both folded and unfolded data, and does not require time-warping for waveform alignment. Written in Matlab, the performance of the supervised classification code is quantifiable and consistent, and the rate at which new data is processed is dependent only on the computational processing power available.

Credit: Johnston, Kyle B. ; Peter, Adrian M.

Site: https://github.com/kjohnston82/SSMM
http://adsabs.harvard.edu/abs/2017NewA...50....1J

Bibcode: 2018ascl.soft07032J

ID: ascl:1807.032
Last edited by Ada Coda on Sun Jul 14, 2019 8:18 am, edited 1 time in total.
Reason: Updated code entry.

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