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SUMMARY:Designed and trained fuzzy logic systems: an interpretable machine
  learning tool - Samuel Morillas Gómez\, Universidad Politécnica de Vale
 ncia
DTSTART:20211118T140000Z
DTEND:20211118T150000Z
UID:TALK165943@talks.cam.ac.uk
CONTACT:104848
DESCRIPTION:Fuzzy theory is an old paradigm that has historically been app
 lied in many fields of science and engineering. It provides a simple frame
 work to implement systems able to codify imprecise human knowledge in term
 s of implication rules. This allows for human knowledge to be easily trans
 formed into a workable algorithm. Lately\, this approach lost interest in 
 the big data era when we have plenty of data but sometimes lack of knowled
 ge\, which is key to design fuzzy logic based systems.\nHowever\, recently
 \, the approach to build fuzzy logic systems is changing: instead of creat
 ing them from human knowledge\, big amounts of data are used to train the 
 system\, that is: to find the implication rules that link inputs and outpu
 ts and tune their parameters. In this talk\, we will review the basics of 
 fuzzy logic and provide some insights about how fuzzy logic systems can be
  learned from data. Also\, an application of a trained fuzzy logic system 
 for spectral curve\nrecovery will be presented.
LOCATION:SS03
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