Adaptive predictors based on probabilistic SVM for real time disruption mitigation on JET
Abstract
Detecting disruptions with sufficient anticipation time is essential to undertake any form of remedial strategy, mitigation or avoidance. Traditional predictors based on machine learning techniques can be very performing, if properly optimised, but do not provide a natural estimate of the quality of their outputs and they typically age very quickly. In this paper a new set of tools, based on probabilistic extensions of support vector machines...
Paper Details
Title
Adaptive predictors based on probabilistic SVM for real time disruption mitigation on JET
Published Date
Mar 2, 2018
Journal
Volume
58
Issue
5
Pages
056002 - 056002
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